This project is for a sentiment analysis web app that predicts the positive sentiment of a piece of text.
The machine learning model used to make predictions was the multinomial Naive Bayes classifier from scikit learn. The model was trained on the Twitter Sentiment Analysis Training Corpus dataset. It contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment.
The model was saved using dill. Dill is a python module which can be used to store python objects to a file, and also send the objects across a network as a byte stream.
The web app was created using Flask. Flask is a microframework for python that allows users to build websites and web apps easily. The web app was deployed onto the internet using Heroku. The url of the web app is https://sentiment-analyser-abiye.herokuapp.com/index