E-commerce Product Price Monitoring and Comparison using Sentiment Analysis

Abstract

The increasing prevalence of e-commerce has empowered consumers with vast choices and opportunities for online shopping. This research paper focuses on two essential aspects of online shopping: price comparison and sentiment analysis of product reviews. The paper presents a methodology for scraping product prices from multiple e-commerce websites and conducting sentiment analysis on the corresponding product reviews. The findings of this research have significant implications for both consumers and e-commerce businesses. Consumers can leverage price comparison data to identify the most cost-effective platforms for their desired products, while sentiment analysis enables them to assess the overall satisfaction levels of other customers. E-commerce businesses can utilize these insights to optimize pricing strategies, identify areas for improvement, and enhance customer experiences. Performance analysis of Support Vector Machine, Logistic Regression, VADER Lexicon and SentiWordNet Lexicon is also done.