Movie Recommendation System using Sentiment Analysis in ML

Abstract: This paper investigates the creation of a sentiment analysis and cosine similarity-based movie recommendation system. By offering tailored movie suggestions based on user preferences and sentiment-driven insights from movie reviews, the suggested method seeks to increase user satisfaction. This technique provides a reliable way for relevant and accurate movie recommendations by combining sentiment analysis to understand the emotional tone of reviews with cosine similarity to quantify the similarities of user profiles. The combined approach's effectiveness is demonstrated by the experimental results, which also illustrate the potential for considerable improvements in user engagement and experience in movie recommendation platforms. The study explores a number of sentiment analysis topics, such as how reviews are categorized into positive, negative, and neutral sentiments and how this affects the recommendation process. Cosine similarity integration makes it possible to compare user profiles based on past preferences, guaranteeing that recommendations closely correspond with user preferences.