Fake Product Review Detection Using Machine Learning

Abstract

Online reviews play a crucial role in determining whether a product will be sold on e-commerce websites or applications. Because so many people rely on internet evaluations, unethical actors may fabricate reviews in order to artificially boost or devalue items and services. To detect false product reviews, this research provides a semi-supervised machine learning approach. Furthermore, feature engineering techniques are used in this work to extract diverse reviewer behaviors. This study examines the outcomes of numerous experiments on a real food review dataset of restaurant reviews with attributes collected from user behavior. In terms off-score, the results indicate that Random Forest surpasses another classifier, with the best f-score of 98 %. In addition, the data reveals that taking into account the reviewers' behavioral characteristics raises the f-score and the final accuracy has come out 97.7%. In the current technique, not all reviewers' behavioral characteristics have been considered. Other low-level features such as frequent time or date dependency, the reviewer's timing for giving a review, and how common it is to deliver favorable or poor reviews will be added further in order to improve the efficacy of the offered fake review detecting algorithm.