A Novel Approach to Enhance Movie Recommendations through Hybrid Collaborative Filtering and Sentiment Analysis

The research work focuses on the movie recommendation system, and it targets the challenges of movie recommendation. This method is more efficient, and also has a higher accuracy compared to regular collaborative filtering typically used. The system uses well-designed sentiment analysis models trained with Natural Language Processing (NLP) considering a huge dataset of user reviews, ratings, and textual parts. Their approach effectively distinguishes sentiments of both types from that species of films, thus compensating the existing limitation in the literature on understanding content-based filtering. The combination of these methods changes the way an audience will choose a movie and approaches recommendations that match most individual user tastes. Finally, we also introduced some new customized recommendation models to improve the movie experience for users. This paper illustrates the prospects of using advanced algorithms such as deep learning and reinforcement learning in building automated, adaptive recommendation systems which are more user-centric. The model developed has secured high accuracy, precision and recall than the traditional approaches.