Abstract
This project introduces an innovative system for analyzing sentiment and detecting fake comments within e-commerce product reviews, aimed at enhancing the credibility and reliability of online feedback essential for consumer purchasing decisions. Our system integrates VADER, a robust rule-based model for general sentiment analysis, with RoBERTa, an advanced transformer-based deep learning model known for its superior accuracy. This dual approach harnesses the complementary strengths of traditional and modern techniques, resulting in a comprehensive and effective solution for sentiment analysis. A critical component of our system is the fake comment detection module, which utilizes sentiment scores and additional indicators to identify fraudulent reviews accurately. This module is essential for ensuring the authenticity of customer feedback and maintaining the integrity of online review platforms. Our analysis uncovers significant trends in sentiment across various product categories, providing valuable insights that can inform targeted marketing strategies and product enhancements. By examining the relationships between sentiment polarity, review ratings, and the likelihood of fake comments, we offer actionable intelligence for e-commerce platforms to improve product quality, enhance customer satisfaction, and safeguard their review systems. This project underscores the importance of advanced tools in upholding the trustworthiness of online reviews. By enabling businesses to understand genuine customer sentiment better and fostering transparency in the digital marketplace, our system enhances the overall consumer experience. Future efforts will focus on refining detection algorithms, exploring additional features for sentiment analysis, and validating the system across diverse datasets and languages to further elevate standards in online review systems.
Published Version
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