Fake Review Detection Using Machine Learning

Abstract

E-commerce and online shopping is a booming industry, especially during the COVID-19 pandemic. Online shopping allows customers to browse and purchase products from home using just a phone or laptop. Customers often never end up seeing their product in real life before the purchase, and are instead dependent on photos and descriptions uploaded by the seller. Thus comes the need for customer reviews: an evaluation of a product by other buyers which can influence other shoppers’ decisions to make the purchase. However, reviews can be tainted to provide a fake or unrealistic depiction of a product. Sellers can pay people or robots to leave fake reviews under competitors or their own stores to increase/decrease sales turnout. Such reviews can be harmful to the buyer or other sellers, often ending up with an unhappy customer. Using supervised datasets consisting of real and fake reviews, we can train a variety of machine learning and deep learning models to recognize attributes differentiating between the two types of reviews. Big e-commerce platforms such as Amazon, Yelp, and Tripadvisor are all common targets of fake reviews, and the implementation of fake review detection could create a more assuring shopping experience for customers. In this paper, we analyze and break down customer review data and attempt to build models that form conclusions from it.