Abstract

 

Traditional recommendation systems primarily rely on user behavior, ratings, and content-based preferences to suggest products or services. However, they often overlook the nuanced emotional context that significantly influences consumer decision-making. This paper proposes a Sentiment-Enhanced Recommendation System (SERS) that integrates sentiment analysis with collaborative and content-based filtering to better capture the affective dimensions of user preferences. By analyzing user-generated content such as reviews, comments, and social media posts using deep learning-based sentiment classifiers, the proposed model quantifies emotional polarity and intensity. These sentiment signals are then incorporated into the recommendation pipeline using hybrid matrix factorization and attention mechanisms, enabling dynamic adaptation to users' emotional states. Experimental evaluations conducted on datasets from Amazon and Yelp demonstrate significant improvements in precision, recall, and user satisfaction scores compared to traditional models. The findings highlight the critical role of emotions in shaping consumer behavior and underscore the importance of affect-aware personalization in modern recommendation systems.