Fake Product Review Monitoring And Removal

Abstract

In the era of e-commerce and digital marketing, online product reviews play a crucial role in shaping consumer decisions and brand reputation. However, the growing prevalence of fake or misleading reviews has led to distorted perceptions and loss of trust among customers. This research project focuses on the development of a Fake Product Review Monitoring and Removal System that identifies and eliminates deceptive reviews using advanced data analysis and machine learning techniques. The system aims to analyze textual features, reviewer behavior, and sentiment patterns to detect anomalies that indicate spam or fraudulent reviews. Natural Language Processing (NLP) is employed to classify reviews as genuine or fake based on linguistic cues and semantic patterns. The proposed system helps e-commerce platforms maintain transparency, improve user trust, and enhance the overall reliability of online review systems. Furthermore, automated detection and removal of fake reviews reduce manual effort and ensure an authentic shopping experience for consumers.