An Efficient Machine Learning System using Sentiment Analysis for Movie Recommendations

In this fast-paced modern world, everyone needs some kind of entertainment in order to maintain a positive attitude and maintain his or her energy levels. The ability to rebuild our self-confidence and feel happy about our profession is made possible by the use of entertainment. When a person needs to refresh himself, he may watch either movies, web series, or anime of his choice, or can listen to his favorite music. The movie suggestion systems can be applied in order to watch the chosen movies online, and these systems are dependable since it reduces the time required looking for desired movies, which cannot be afforded to squander. This paper implements content-based filtering using cosine in order to enhance the performance of a movie recommendation system. Comparative results have been demonstrated, and similarities in the suggested methodology are presented. Both of these findings reveal that the proposed strategy outperforms pure approaches in terms of accuracy, quality, and scalability of the movie recommendation system across three different datasets. The “preferred” that a user would give to a system is predicted by a recommendation system, which is a subclass of information filtering systems. These systems base their predictions on information from users. Their most common applications are in the corporate world. The Movie Recommendation System helps users quickly and effectively find movies that interest them based on either their own prior movie experiences or the movie experiences of other users, all without having to waste time looking for or scrolling through films that are irrelevant to them.