Unlocking Customer Insights: A Review of Sentiment Analysis Approaches for Product Reviews with Performance Benchmarking.

The exponential growth of user-generated content on e-commerce platforms has emphasized the critical role of sentiment analysis for interpreting consumer feedback. This paper provides a systematic review of sentiment analysis techniques, categorizing them into lexicon-based methods, machine learning algorithms, deep learning architectures, and hybrid or ensemble strategies. Each method is evaluated based on its theoretical framework, suitability for analyzing product reviews, and comparative performance across diverse datasets. Emphasis is placed on deep learning and ensemble models due to their capacity to capture contextual and semantic complexities more effectively. Performance benchmarks drawn from existing literature highlight key trends, limitations, and methodological innovations in sentiment classification. These insights support the development of robust and scalable sentiment analysis systems for product review interpretation.