Advancing Sentiment Analysis on Product Reviews: A Comparative Evaluation of Classical, Deep Learning, and Transformer Models

The proliferation of e-commerce platforms has led to an explosion of user-generated content, particularly in the form of product reviews. These reviews offer valuable insights into consumer sentiment, influencing business strategies and customer satisfaction initiatives. This study undertakes a comprehensive evaluation of sentiment analysis models applied to product reviews, spanning traditional machine learning algorithms, deep learning architectures, and state-of-the-art transformer-based models. The models evaluated include Logistic Regression, Support Vector Machines, Decision Trees, Random Forests, Naïve Bayes, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and advanced transformers such as ELMo, DistilBERT, ELECTRA, T5, BERT, and RoBERTa. Using a standardized Amazon product review dataset, models were assessed on accuracy, precision, recall, and F1-score. Results indicate that transformer-based models significantly outperform their predecessors, with RoBERTa achieving the highest accuracy of 96.36%. These findings underscore the growing importance of transformer architectures in sentiment classification, offering promising directions for real-time applications in e-commerce, social analytics, and recommendation systems.