Detection of Fake Product Review using Machine Learning Techniques

Abstract

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 online reviews, unethical actors may create reviews in order to artificially boost or devalue goods and services. This study presents a semi-supervised machine learning strategy to identify fake product reviews. In addition, this work extracts several reviewer behaviours using feature engineering techniques. In this work, we investigate the findings of several experiments on a genuine food review dataset of restaurant evaluations with features gathered from user behaviour. The results show that Random Forest, with the best f-score of 98%, outperforms another classifier in terms of off-score. The data also shows that accounting for the reviewers' behavioural traits boosts the f-score, and the final accuracy came out at 97.7%. The behaviour of different types of reviewers has not been taken into account in the existing methodology. The effectiveness of the proposed fake review detecting algorithm will be further enhanced by the addition of other low-level data, such as frequent time or date dependency, the timing of the reviewer's delivery of a review, and how typical it is to supply favourable or unfavourable reviews.