Year: 2025
Type: article
Abstract: Product reviews are crical to onlone shopping but false are deceptive reviews undermine producer confidence and undermine the legitimacy of the product . This study presents a usimg machine learning to detect bogus reviews utilizing, category, and rating data. The method includes TF-IDF for text features, One Hot Encoding for categories, and uses an XGBoost classifier for prediction. Model performance is measured using precision, recall, and F1-score. A Streamlit web app is also developed for real-time detection. Results show the system is precise and trustworthy in spotting fake reviews..
Authors: Rakshitha R, G Prasanna David
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416412849