User feedback can be a valuable asset to organizations that are interested in learning about the general population's interests and usage about their products, products that they may have just launched or plan to modify or enhance. User ratings are something that cannot be totally relied upon, since users generally tend to make an effort and leave a review online if the product doesn't reach their expectations.
This project code uses NLTK (Natural Language Toolkit) python library for statistical natural language processing and analyzes the user input statements (or user feedback) to determine if they make a positive, negaitive or neutral sense based on sentiment and polarity scores.
The Google colab notebook can be found here.
An updated version of this project with a deployed web app can be found here.