Unmask inflated product reviews through Machine Learning

Abstract

Product reviews play a crucial role in online customer purchase decisions. In today’s marketplaces, such as Amazon, the quantity and interest of product reviews is growing, along with fierce competition between sellers. At the same time, the phenomenon of fake reviews is ever-growing. Many proposals capable of detecting fake reviews based on Machine Learning (ML) exist in literature. Nevertheless, bad practices implemented by the sellers make genuine reviews difficult to recognize. For instance, some Telegram Channels are known for giving products for free in exchange for 5-star reviews. This work focuses on the analysis of two review data streams of Amazon products. The first one is composed of the reviews corresponding to the products in the AmazonBasics category. The latter collects reviews of the products in the Telegram channels mentioned above. The analysis reveals a substantial dissimilarity between the two sources of reviews, that conducts to the construction of a ground truth dataset employed in the classification model training. The classification activity can assist during product rating interpretation, which could be invalidated by too many fake reviews. The experimental results reveal that 1&2-star reviews are good predictors of the review’s trustworthiness and the product itself.