Counterfeit Review Rating and Ranking Fraud Detection

Abstract

Nowadays the online market has been a recent trend all over the world. They provide all products and services to all people. These products and services are greatly trusted with the help of reviews on sites. From software applications to hardware products everything has their own review. Some of the product creators modify the ratings by giving false review to their product. The people who see such review gets confused and uses them and feel unworthy about the product sometimes about the online market. By giving the false review the product takes top place in ranking. But there is a great demand for getting rid from these review frauds. Some solutions focus on the classification of opinions using the natural language processing and data mining. We have a new way to get rid from these review frauds. We investigate the evidences by modelling the products and services based on the behaviour. By collection the information and by the analysis we and find the review frauds. We use the OLAP data aggregation for online reporting mechanism for information process. All feedbacks from user are collected. User can able to differentiate the fake and original products and services. In addition, the user interest can also be recorded in this method. This is employed by data mining and artificial intelligence to find the fake profiles and their commands