Fake Product Review Monitoring System Using Machine Learning

Abstract

The vast majority of consumers need to read positive product reviews before making a purchase decision. Users can't tell if the reviews they're reading are authentic or fraudulent since there's no way to verify their veracity. Some review sites have been shown to have fabricated favourable assessments of their products by employing employees from the product's parent firm. Many of their own company's items have received glowing praise from reviewers. The review's veracity will be hidden from the user. This "Fake product Review monitoring and removal system" was created to eliminate the difficulty of discovering and eliminating false reviews on a website. By analysing review posting habits and IP addresses, this system may detect users who intentionally submit false reviews or comments on a product. A user may browse items and provide feedback on them after logging in to the system using his user id and password. If the system detects that a certain IP address has submitted several fraudulent reviews, it will alert the administrator to the problem and delete the reviews in question. The system makes advantage of data mining techniques. The user is aided in their search for an honest product review by this approach.