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?
- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.
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.
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.
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.
Here's the code implementation with Streamlit App for the users.
- Enter the text.
- Hit the submit button.
- Tada!! Get the output in visual form.
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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