Metadata-Version: 2.1
Name: text2emotion
Version: 0.0.4
Summary: Detecting emotions behind the text, text2emotion package will help you to understand the emotions in textual meassages.
Home-page: https://github.com/aman2656/text2emotion-library
Author: text2emotion Team
Author-email: Text2Emotion@gmail.com
License: MIT
Description: # What is emotion?
        Emotion is a biological state associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
        (Source: Wikipedia)
        
        Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?
        
        #### text2emotion is the python package which will help you to extract the emotions from the content.
        
        - Processes any textual message and recognize the emotions embedded in it.
        - Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.
        
        ## Features
        > ##### 1. Text Pre-processing
        > At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
        > - Remove the unwanted textual part from the message.
        > - Perform the natural language processing techniques.
        > - Bring out the well pre-processed text from the text pre-processing.
        > ##### 2. Emotion Investigation
        > Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
        > - Find the appropriate words that express emotions or feelings.
        > - Check the emotion category of each word.
        > - Store the count of emotions relevant to the words found.
        > ##### 3. Emotion Analysis
        > After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
        > - The output will be in the form of dictionary.
        > - There will be keys as emotion categories and values as emotion score.
        > - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.
        
        ## How to use?
        #### [Check Demo on Colab](https://bit.ly/3hlXujZ)
        
        ## App Deployment
        Here's the code implementation with **Streamlit App** for the users.
        1. Enter the text.
        2. Hit the submit button.
        3. Tada!! Get the output in visual form.
        #### [Check Demo of App](https://text2emotion.herokuapp.com/)
        
        Let's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
