CineInsight: NLP-Driven Movie Recommendation Enhancement for Over-The-Top Platforms

Computers, through language, understand, interpret, and interact with humans, enabling Natural Language Processing (NLP), a sector of Artificial Intelligence (AI). Selecting a movie to watch may be very difficult because so many options are accessible on different streaming services; people have different likes and preferences, and others are unaware of fantastic films. Objective: In today's digital landscape, creating and executing a movie recommendation system is crucial to addressing the issues of information overload, improving user satisfaction, and maintaining competitiveness in the entertainment sector. Method: This study proposed the CineInsight NaiveFlix Algorithm for a movie recommendation, which leverages a movie review dataset gathered from the websites of YIFY and IMDB. After that, the preprocessed data was pipelined, and the essential stop words in the English language were extracted to improve the Naïve Bayes (NB) model. Subsequently, the audience reviews of the film were categorized as either positive or negative. Results: After comparing the suggested method's performance to the conventional NB model and the linear support vector classification algorithm, it was discovered that the suggested CineInsight NaiveFlix method performs better in categorizing audience movie reviews.