Sentiment Analysis-Based Product Review System for Enhanced Recommendations

The rapid growth of e-commerce has resulted in an overwhelming amount of user-generated content in the form of product reviews. Harnessing these reviews effectively is critical for enhancing recommendation systems. This paper presents a sentiment analysis-based approach that delves into specific product attributes, such as quality, design, price, durability, usability, comfort, value for money, and customer service, to extract actionable insights. By employing natural language processing (NLP) techniques, our system calculates both overall and attribute-specific sentiment scores, which are aggregated into a comprehensive recommendation score. The proposed methodology bridges the gap between unstructured text reviews and structured product recommendations, offering a detailed, attribute-focused perspective to improve user decision-making.