E-commerce platforms
Help shoppers evaluate the language behind product feedback.
TECHNOLOGY WITH A PURPOSE
Bringing a little more trust to reviews.
FakeReview AI · AI-Powered Fake Product Review Detection System
Academic AI/ML project
FAKE PRODUCT REVIEW DETECTORBetter decisions begin
Better decisions begin
with better information.
Reviews influence what we buy. This project explores how intelligent text analysis can help us question misleading feedback and understand the patterns behind online opinions.
Explore the review analyzerProblem Statement
Online shopping platforms contain fake, manipulated, or misleading reviews that can influence customers' purchasing decisions.
Star ratings alone do not tell the whole story. Suspicious language patterns can offer useful context, but cannot establish authenticity.
Objective
Develop an intelligent system capable of analyzing review text and predicting whether a review is likely to be genuine or suspicious.
Present predictions with clear explanations, honest limitations, and an approachable interface suitable for academic demonstrations.
WHERE IT CAN HELP
Small signals. Broad applications.
Potential uses for a validated model, with human judgment kept in the loop.
Product review websites
Provide context alongside review summaries and ratings.
Consumer protection
Support more informed and thoughtful purchasing decisions.
Brand monitoring
Explore patterns in product feedback and public perception.
Review moderation
Surface reviews for human attention, never automatic accusations.
Built to learn. Ready to evolve.
A modular React frontend separates presentation, review analysis, and sample data. The demo runs locally; a prediction adapter provides a clear connection point for a future ML service.
PREDICTION RESPONSE CONTRACT
{
"prediction": "fake",
"confidence": 0.92,
"fake_probability": 0.92,
"genuine_probability": 0.08
}Transparency is part of the project, not an afterthought. Demo results are never presented as real ML predictions.