MOVIE RECOMMENDATION SYSTEM BASED ON TWITTER SENTIMENT DATA

Abstract

The use of recommendation systems (RSs) in e-commerce and digital media has attracted a great deal of interest. Collaborative filtering (CF) and content-based filtering (CBF) are examples of traditional approaches in RSs. These systems have some drawbacks, such as the requirement of prior user history and habits for executing the task of recommendation. This article suggests a hybrid RS for movies that makes use of the finest ideas from CF and CBF as well as sentiment analysis of tweets from microblogging websites in order to lessen the impact of such limitations. The goal of using movie tweets is to comprehend current trends, popular opinion, and user reaction to the film. On the public database, experiments have produced encouraging outcomes.