Movie Recommendation system with sentiment analysis using deep learning algorithms

Abstract

In the era of digital media saturation, recommendation systems have become essential tools for delivering personalized content to users. While traditional approaches rely on user–item interactions and content similarity, they often overlook the emotional nuances expressed in user reviews. This study presents a sentiment-aware hybrid recommendation system that integrates deep learning-based sentiment classification with user demographics and item features to enhance movie recommendation accuracy. The proposed model employs Bidirectional Encoder Representations from Transformers (BERT) to classify user reviews into five nuanced sentiment polarities viz positive, slightly positive, neutral, slightly negative, and negative. These sentiment scores are embedded into a Deep Factorization Machine (DeepFM) architecture, which captures complex relationships among users, items, and emotional cues. A multi-filtering strategy incorporating user age, gender, occupation, location, and movie genre is utilized to mitigate cold-start problems and refine recommendations. Experimental evaluation using the MovieLens dataset, complemented with IMDb user reviews, demonstrates improvements in ROC-AUC (84.47%), Balanced Accuracy (76.36%), and PR-AUC (82.13%) compared to traditional systems. The findings highlight the effectiveness of integrating fine-grained sentiment analysis into the recommendation process, offering deeper insights into user intent and improving the personalization of suggestions. The proposed framework presents a scalable and efficient solution for building emotionally intelligent recommendation systems, fostering deeper user engagement, informed decision-making, and more meaningful media experiences.