Integrating machine learning and sentiment analysis in movie recommendation systems

Abstract

Abstract The fast growth of the film business, along with an ever-increasing number of movie options, has highlighted the need for better recommendation algorithms. This study investigates the application of sentiment analysis in a movie recommendation system with the goal of improving the user experience. The importance of this sector stems from its ability to bridge the gap between user interests and the vast number of cinematic products, addressing individual emotional states and preferences. Researchers choose to generate movie recommendations based on the sentiments conveyed by viewers’ reviews of the movies. Sentiment-based movie recommendation system research employs techniques such as natural language processing and hybrid models with the goal of increasing user satisfaction. To this purpose, the integration of advanced machine learning algorithms such as cosine similarity, support vector machine, and Naive Bayes improves recommendation systems with sentiment analysis. Cosine similarity improves movie recommendations by recognizing minor user preferences, while support vector machines and Naive Bayes enhance sentiment analysis by offering a nuanced interpretation of textual attitudes. Through trials, the proposed system employs two public datasets for sentiment analysis, namely the TMDB5k dataset and the Reviews dataset, and makes predictions (positive, negative, or neutral) based on the content of the review through conducting sentiment analysis on the text using the Viscous Accretion Disk Evolution Resource (VADER) approach. The findings, based on users’ feedback, are more accurate and informative regarding movie quality, where SVM accuracy is 99.28% and Naïve Bayes accuracy is 96.60% when used with VADER sentiment analysis.