Metadata-Version: 2.1
Name: cleantweet
Version: 2.0.2
Summary: a python library to clean textual data fetched from API's
Home-page: https://cleantweet.org/
Author: Lare Adeola
Author-email: lare@cleantweet.org
License: MIT
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: nltk
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: wordcloud

CleanTweet version 2.0.2



CleanTweet is a python library created for cleaning textual data fetched from API. In the second version of the cleantweet library, there are 6 new classes that have been introduced. They include the DiagramTweet

PredictionTweet, SaveTweet, SentimentTweet, StatsTweet, and SyntheticTweet. Each of these classes have different methods specific

to each class and most of them inherit the clean() method from the CleanTweet class which was the only class present in the version

0.1.1. 



You can find more detailed documentation about the various classes, their implementation and their various methods (close to 50) in

cleantweet's permanent home: https://cleantweet.org/



### Installation

pip install cleantweet <br/>

Get Started <br/>

How to clean your Twitter Object:



Example 1: If the text file containing the Twitter/X JSON Data is in the same directory as project files.



!pip install cleantweet <br/>

import cleantweet as clt <br/>



### Instantiate the CleanTwitter Object

data = clt.CleanTweet('sample_text.txt')



### Call the clean method

data = data.clean())



### Print the data object

print(data)



## Various Implementations for the new classes



### Draw a Wordcloud from your textual data



import cleantweet as clt



diagram = clt.DiagramTweet('sample_text.txt')

diagram.word_cloud()



### Amount of Plural Proper Nouns in your text



import cleantweet as clt



text = clt.StatsTweet('sample_text.txt')

text = text.amount_of_proper_nouns(plural=True)

print(text)



### Sentiment Analysis



import cleantweet as clt



sentiments = clt.SentimentTweet('sample_text.txt')

sentiments = sentiments.vader()

print(sentiments)



### Synthetic Data



import cleantweet as clt



synths = clt.SyntheticTweet('sample_text.txt')

synths = synths.rule_based()

print(synths)
