Book Recommendation Through Sentiment Analysis Using Machine Learning

The rapid increase of digital content in today’s technological age calls for the creation of accurate and individualized book recommendation systems (BRS) to improve user interaction and satisfaction. This paper presents a unique recommendation system that merges traditional collaborative filtering methods with sophisticated sentiment analysis. The objective is to provide customized book recommendations based on user interests and emotional state. The methodology involves analyzing user interactions, historical data, and emotional feedback extracted from user-generated content. By using machine learning and natural language processing, the proposed system effectively captures user sentiment nuances, resulting in improved recommendation accuracy and relevance. The results indicate that including sentiment analysis provides a deeper understanding of user preferences in context, with an observed accuracy improvement of approximately 15% over conventional recommendation methods. This work not only contributes to existing knowledge on recommendation systems but also provides valuable insights for developers and researchers looking to enhance user experiences on digital reading platforms. The findings highlight a significant advancement in customizing book recommendations through the integration of emotional and contextual signals, laying a foundation for future studies in this domain.