Classification and Analysis of Fake Product Review using Ai

Resumen

In today's e-commerce, recommender systems play an essential part in decision-making.
Customers, for illustration, check product or store reviews before determining what to
purchase, where to buy it, and whether or not to buy it. Because there is monetary reward in
submitting fake/fraudulent reviews, there has been a large surge in difficult opinion spam on
online review platforms. In essence, an untruthful review is a phoney, fraudulent, or opinion
spam review. Positive ratings of a specific object can attract more consumers and improve
sales; bad evaluations might reduce demand and sales. In recent years, fake review detection
has received a lot of attention. However, most review sites still do not openly screen bogus
reviews. Yelp– is an exception over the past few years. The detection of phoney internet
reviews has become a prominent research topic as a result of the increasing use of fake reviews.
Despite earlier research' efforts to detect phoney reviews, the concerns of imbalanced data and
feature trimming remain unaddressed. The approach presents an ensemble approach for
detecting false online reviews to fill in these gaps.