Fake Product Review System


Authors : Atul Kumar; Garima Tyagi; Pawan Yadav; Piyush Kumar Yadav

Volume/Issue : Volume 7 - 2022, Issue 3 - March

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3rHNAR0

DOI : https://doi.org/10.5281/zenodo.6476720

In this day and age, surveys on web-based sites play an important role in product sales because people try to get all of the advantages and disadvantages of any item before purchasing it because there are various options for a similar item, such as different makes for a similar type of item, or differences in merchants that can provide the item, or differences in the method used to purchase the item, so the audits are important, because it's difficult for them to personally verify each item and sale, a tool called Fake Review Detection is used to detect any fraud. The client made the request only based on the rating and examining the audits associated with the specific item. Others' comments provide a wellspring of satisfaction for the new goods customer. It's possible that a single unfavourable audit will persuade a customer not to buy that item. In the current situation, it's possible that this one audit is bogus. Thus, to eliminate phoney audits and provide clients with the first surveys and ratings associated with the items, we proposed the Fake Product Review Monitoring and Removal System (FaRMS), which is an Intelligent Interface that takes the Uniform Resource Locator (URL) associated with Amazon, Flipkart, and Mynntra results and dissects the surveys, providing the client with the first appraising. The suggested framework is unique in that it works with three web-based company websites rather than only breaking down surveys in English. The requested project was completed successfully. The accuracy of 87 percent in recognizing counterfeit surveys written in English was achieved using acute learning methods, which is higher than the precision of previous models.

Keywords : Fake Reviews Detection,, Machine Learning

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