Fake Product Review Monitoring And Removal

Online reviews have become an integral part of the consumer decision-making process. However, the presence of fake or deceptive reviews hinders the reliability and trustworthiness of online platforms. This paper presents GadgetReviewAnalyzer, a web-based application that combines web development using the Django framework with machine learning techniques to provide users with a reliable tool for analyzing customer reviews of electronics gadgets on Flipkart. The system utilizes various machine learning models, including Logistic Regression, SVM, Naive Bayes, to accurately classify reviews as either fake or real. The performance of each model is evaluated, with the Support Vector Machine model achieving the highest accuracy of . The system’s web interface allows users to register, login, select products, input the number of review pages to analyze, and view the authenticity of each review on the home page. Administrators have access to detailed product and review information, including individual reviews from specific customers. GadgetReviewAnalyzer offers a valuable solution to mitigate the impact of fake reviews and empower users to make informed purchasing decisions.