Integrating Sentiment Analysis and Machine Learning for Improved Film Recommendation Systems

Abstract: To do so, especially when it comes to digital entertainment platforms, which are evolving rapidly, giving users options for having what they want, and the importance of personalization has simply gone up only. This project focuses on movie recommendations using sentiment analysis, which increases the accuracy of recommendations by adding review sentiments based on movie ratings and reviews. This project presents a movie recommendation system that leverages collaborative filtering and sentiment analysis to deliver more accurate suggestions to movie enthusiasts, specifically targeting "Cinemaniacs" and "Film Junkies.” The primary components of our approach include collaborative filtering, such as taking movie preferences from the user and performing natural language processing (NLP) techniques for sentiment analysis of user-generated reviews and ratings. By integrating user preferences, such as selected genres and viewing habits, with sentiment-driven insights from movie reviews, the system extends beyond traditional numeric ratings to capture nuanced satisfaction and dissatisfaction. The platform also includes genre-specific recommendations and predictive models that forecast upcoming movie releases based on individual tastes, trends, and pre-release sentiments. This innovative combination provides users with highly customized movie and web series suggestions, keeping them updated on new releases, while offering trailer predictions to preview potential interests. Through this multi-layered approach, the project aims to deepen viewer engagement by closely aligning content recommendations with emotional responses and genre preferences