Enhancing Recommender Systems: A Hybrid Approach Using Sentiment Analysis and Collaborative Filtering

With the development of the Internet and e-commerce, the number of online reviews has increased, creating a large array of data for user decision-making. However, traditional recommender systems often do not take into account the emotional preferences of users, and collaborative filtering methods face the problems of data sparsity and cold start. This paper proposes a recommendation algorithm that combines emotional color analysis of online reviews with collaborative filtering to improve recommendation accuracy. The algorithm analyzes the emotions in user feedback, determines their preferences, and uses this information to generate personalized recommendations. Experiments have shown that the proposed approach improves the accuracy, completeness, and coverage of recommendations compared to traditional methods, especially in the category of similar product recommendations.