This web based application takes a user input and shows the sentiment and polarity scores of the user's input.
The application interface on the home page, when opened, looks like this:
The text box takes user input sequences and classifies them using TextBlob library (an inbuilt python library) which is built on NLTK library to classify a set of predefined words in the dictionary as positive, negative and neutral along with a score associated with the extend of attitude the word expresses.
The user inputs could be words, paragraphs or a couple of paragraphs. The machine can tokenize each sentence/paragraph into individual tokens and tries to analyze and reflect if the set of sequences try to make a positive, negative or neutral sense.
A simple example of how this app functions is shown below when the user inputs a sentence "I love Emma's cat. I wish I could take him home.":
The application also has an about page that describes all the tools used while developing the program. The about page is shown below:
Got some cool feature suggestions or improvements?
Feel free to create a pull request on GitHub to report it to me :)