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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.


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

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

Let's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.


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