Sentiment Analysis Approach through Artificial Intelligence Based Machine Learning for Movie Review

Movies have evolved into a powerful medium for advertising and marketing, with the power to shape consumer habits and preferences. With the ability to influence consumer habits and preferences, films have developed into a potent medium for marketing and advertising. More specifically, movie reviews have the ability to affect the decisions of those who may see them. Nonetheless, customers have the difficult task of gleaning valuable insights from many evaluations and sifting through a deluge of reviews to identify the relevant and helpful ones. The grouping of many models according to performance indicators like as f1-score, recall, accuracy, and precision. In a comparison of models, BERT achieves the greatest performance accuracy 90.67% with 0.88% precision, 0.92% recall, and 0.88% f1-score among a compared techniques. Research into an interpretability of DL models for sentiment analysis in movie reviews, the exploration of new feature extraction approaches, and improving model generalization across varied datasets are all potential areas for future work.