Intelligent Interface for Fake Product Review Monitoring and Removal
Author(s):
E.MADHORUBAGAN, TAMILSELVI .V, SOWMIYA .M, SNEHA .S
Keywords:
Abstract
As the trend to shop online is increasing day by day and more peopleare interested in buying the products of their need from the online stores. This type of shopping does not take a lot of time of a customer. Customer goes to online store, search the item of his/her need and place the order. But, the thing by which people face difficulty in buying the products from online store is the bad quality of the product. Customer place the order only by looking at the rating and by reading the reviews related to the particular product. Such comments of other people are the source of satisfaction for the new product buyer. Here, it may be possible that the single negative review changes the angle of the customer not to buy that product. In this situation, it might possible that this one review is fake. So, in order to remove this type of fake reviews and provide the users with the original reviews and rating related to the products, we proposed a Fake Product Review Monitoring and Removal System (FaRMS) which is an Intelligent Interface and takes the Uniform Resource Locator (URL) related to products of Amazon, Flipkart and Daraz and analyzes the reviews, and provides the customer with the original rating. It is a unique quality of the proposed system that it works with the three e-commerce Websites and not only analyzes the reviews in English but also the reviews written in Urdu and Roman Urdu. Previous work on fake reviews does not support feature to analyze the reviews written in languages like Urdu and Roman Urdu and cannot handle the reviews of multiple e-commerce Websites. The proposed work achieved the accuracy of 87%in detecting fake reviews of written in English by using intelligent learning techniques which is greater than the accuracy of the previous systems.
Article Details
Unique Paper ID: 151055

Publication Volume & Issue: Volume 7, Issue 11

Page(s): 486 - 499
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