Natural Language Processing Based on Movie Rating System Using Microblogging

Movie recommendation systems help users quickly find movies that match their preferences, similar to platforms like Netflix, which personalize suggestions based on individual viewing habits. As digital content grows exponentially with technological advancements, users face challenges in discovering movies that align with their taste, sentiment, and genre. To address this issue, various software solutions have been developed to improve movie recommendations. However, traditional recommendation methods, such as content-based and collaborative filtering, often struggle to deliver highly personalized suggestions. To enhance accuracy, this system leverages advanced sentiment analysis techniques to evaluate user reviews and align recommendations with individual preferences. By incorporating multiple algorithms, the system improves personalization and ensures an intuitive, user-friendly interface for a seamless movie discovery experience.