Fake Product Review Detection and Removal Using Opinion Mining Through Machine Learning

Abstract

Machine learning is one of the growing trends in artificial intelligence and deep learning scenarios where the machine learns to acquire data from previous cases and implements the data for future prediction and analysis. The objective of this chapter is the detection and removal of fake reviews in online reviews. Majority of online buyers rely on product reviews before making purchase decision of their chosen brand; however, fake reviews pose a continuous threat to the integrity of the product, portals and the easy-to-find reviews on specific products. This chapter aims to develop a system to identify and remove fake reviews with the view of protecting the interests of customers, products and e-commerce portals. Thus, in this proposal, the primary goal is detecting unfair reviews on Amazon reviews through Sentiment Analysis using supervised learning techniques in an E-commerce environment. Sentiment classification techniques are used against a dataset (Amazon) of consumer reviews for smartphone products. Precisely, we use three different algorithms, logical regression algorithm, linear regression algorithm and neural networks (CNN and RNN models), of supervised machine learning technique to find similarities in the review dataset and group similar datasets together to explore unfair and fair positive and negative reviews, which involves screening, collaborative filtering, and removing with an optimal accuracy rate. The core focus or the highlight of this chapter is to explore an algorithm using deep learning that ensures optimal accuracy in the identification of fake reviews.