Survey on Various Tool for Analyzing and Detecting Fake Review by using AI

Abstract

The prevalence of fake product reviews in online shopping platforms has raised concerns regarding the authenticity and reliability of customer feedback. This study proposes a method for detecting fake reviews using a logistic regression model and review-centric features. By analyzing linguistic patterns, sentiment analysis, and reviewer behavior, the proposed model aims to accurately differentiate between genuine and fake reviews. Experimental evaluations on real-world datasets demonstrate an overall accuracy of 82%, showcasing the effectiveness of the approach. The integration of natural language processing techniques allows for the extraction of important features from review texts, which are then utilized as inputs to the logistic regression model. Additionally, considering reviewer behavior, such as the number of reviews, consistency of ratings, and timing, enhances the accuracy of the detection system. The proposed method offers a scalable solution that can be easily implemented in existing e-commerce platforms, bolstering their credibility and protecting consumers from misleading information. By filtering out fake reviews, this approach empowers users to make informed purchasing decisions, ultimately improving customer satisfaction in the online marketplace