MOVIE RECOMMENDER SYSTEM USING SENTIMENT ANALYSIS

Abstract

Today, recommendation systems are among the most crucial AI tools for reaching people with
relevant data. Content-based filtering and collaborative filtering are examples of methods formerly used in RS.
As a result, there are constraints associated with these methods, such as the dependence on users' browsing
histories. This study offers a hybrid RS that combines Collaborative Filtering, Content-based Filtering, and
Movie Sentiment Analysis to compensate for the impact of such dependencies. In this study, we created a
user-emotion-based recommender system to provide movie recommendations based on a user's viewing
habits.