AI-BASED PRODUCT REVIEW SYSTEM

Author(s)	Ms. Nandini Verma, Ms. Satakshi Jangid
Country	India
Abstract	The rapid growth of e-commerce has made online reviews one of the most influential factors in customer decision-making. However, the rise of fake, biased, and bot-generated reviews has severely impacted the credibility of online platforms. This research presents an AI-powered system for automated product review sentiment analysis and fake review detection. The system integrates Natural Language Processing (NLP), Machine Learning (ML), and web scraping techniques to extract, preprocess, analyze, and classify reviews. Sentiment classification is performed using VADER and TextBlob, while fake review detection is implemented through Isolation Forest anomaly detection and linguistic pattern analysis. A Streamlit dashboard is developed to visually present sentiment distribution, fake review flags, and multiproduct comparison charts. Results demonstrate that combining NLP with anomaly detection significantly improves the reliability of online review interpretation. This study contributes to enhancing transparency, improving consumer trust, and helping businesses better understand genuine customer feedback.
Keywords	Artificial Intelligence, Sentiment Analysis, Fake Review Detection, NLP, Machine Learning, Streamlit, Ecommerce Reviews, Isolation Forest
Field	Computer > Data / Information
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-17
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.61978

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