Enhancing OTT Platform Recommendations Through Sentiment Analysis: A Comprehensive Study

Online recommendation engines, especially on OTT platforms like Netflix, significantly influence consumer choices. Companies use these systems to boost engagement by providing product suggestions based on friend recommendations, product comparisons, and user feedback. Netflix, with its extensive library, has transformed TV and movie consumption. However, the vast array of options can be overwhelming, necessitating effective guidance for users. While current recommendation systems have made substantial progress in delivering relevant content, significant challenges remain. A key gap is the ongoing need for improvement in managing diverse applications and user groups. This involves tackling issues such as data sparsity, the cold start problem, and the evolving nature of user preferences. The motivation for this research stems from the persistent demand for more accurate and personalized recommendation systems. This research aims to enhance recommendation systems by improving sentiment analysis techniques. Additionally, the integration of sentiment analysis presents an opportunity to further. By integrating these improvements into recommendation engines, we seek to refine recommendations by understanding deeper emotional responses and subjective viewpoints, and address user preferences on OTT platforms like Netflix or Amazon, ultimately boosting user satisfaction and engagement.