Sentiment Tendency Analysis and Personalized Recommendation of Movie Reviews Driven by Deep Learning

This article offers a general evaluation of personalized recommendation systems and movie review sentiment analysis using recent deep learning (DL) methods. The paper shows how DL models—especially CNNs, RNNs, and transformers such as BERT (Bidirectional Encoder Representations from Transformers)—help to execute advanced sentiment analysis and boost recommendation personalizing with impressively accuracy and recall. These models enable a complete awareness of user comments, so enabling a better fit of user interests with recommendations. Furthermore included as benchmarks to show the progress gained with more modern DL techniques are fundamental techniques such Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The effort largely targets BERT for recording complicated and contextual sentiment trends and their use into recommendation systems to dynamically adjust suggestions depending on user emotions. This approach promotes user involvement and enjoyment by matching recommendations with evolving tastes. Furthermore included in the paper is hyperparameter optimization as a solution for data imbalance and processing efficiency. Underlined as new models stressing their scalability and ability to infer and alter suggestions depending on user emotional states are graph neural networks (GNN) and variational autoencoders (VAE). Our work emphasizes on the necessity of scalable architecture and diverse, current datasets in order to guarantee relevance and sustain consumer satisfaction in AI-powered recommendation systems. By merging sentiment analysis with advanced DL techniques, this work provides a framework for developing more accurate, dynamic, and adaptable recommendation systems across several domains.