Abstract
Twitter is an online social networking and micro blogging site that has millions of users today. Twitter users ‘tweet’ at least twice a day on an average, thus generating a massive amount of data. This project tries to harness the data generated by Twitter into some meaningful information. The objective of this project is to give an accurate rating for a movie based on twitter chatter. A user can search for any movie of his choice. The application uses the REST API provided by Twitter to get the most recent tweets about it. These tweets are classified into positive, negative or neutral tweets using the Naïve Bayes Classification. The retrieved tweets are then stored in the database along with the sentiment associated with it. The rating of the movie is the result of a mathematical formula based on the number of positive, negative and neutral tweets stored in the database. The expected conclusion is that the rating will be more accurate than the rating for a movie when compared to ratings on IMDB, Rotten Tomato or other such websites, since it is designed to reflect the opinion of the people at the time of query.