FAKE PRODUCT REVIEW DETECTION FOR GENUINE ONLINE PRODUCT USING OPINION MINING

Abstract

It is important for future customers to make choices on the basis of online feedback. The utility, though, gives rise to a curse - a false opinion spam. Deceptive opinion spam misleads prospective consumers and organisations to reshape their companies and inhibits opinion-mining strategies from drawing correct conclusions. Thus, the identification of misleading feedback has become more and more forceful. In this project, we try to figure out how to differentiate between fake reviews and genuine by using the linguistic features of the Yelp Filter Dataset. We have suggested an approach for features extraction dependent on the Latent Dirichlet Allocation (LDA). The findings of the experiment have shown that the procedure is efficient. The growing prevalence of online reviews also encourages the false review writing industry, which relates to paying human writers creating disappointing reviews to manipulate the opinions of readers. Our project solves this issue by developing a classifier that takes the evaluation text and its reviewer s specific data as inputs and outputs ifthe review is valid.