An overview of Existing Approaches for Product Review and Analysis

In the current digital age, the vast amount of user- generated reviews on e-commerce platforms often overwhelms consumers, making it challenging to make informed purchasing decisions.”ShopInsight – Product Re- views and Analysis Using ML Algorithms” addresses this problem by providing an intelli- gent, centralized platform that aggregates and analyzes product re- views from multiple online retailers. The rapid growth of e-commerce platforms such as Amazon, Flipkart, Walmart, and Myntra has provided customers with a wide range of product options. However, the availability of the same product across multiple platforms at varying prices, ratings, and reviews makes it difficult for users to identify the best choice. This project proposes the development of a ma- chine learning–based product aggregation and comparison website that integrates product details from multiple e- commerce platforms into a unified interface. The system employs web scraping and API integration to extract key attributes such as product name, price, ratings, reviews, and availability. Natural Language Processing (NLP) tech- niques are applied to normalize and match similar products across different platforms. A ranking algorithm then sorts the aggregated results, primarily based on ratings, ensuring that users are presented with the most reliable options first. The website provides an efficient, user-friendly interface that reduces decision-making time, improves the online shopping experience, and enables cost-effective purchas- ing. Future enhancements may include price-drop alerts, sentiment analysis of customer reviews, and personalized product recommendations.