Detection of spam reviews: a sentiment analysis approach

Abstract

Electronic shopping is highly influenced by online reviews posted by customers against the product quality. Some fraudulent pretenders consider this as an opportunity to write the spam reviews to upgrade or degrade product’s reputation. Hence, detection of those reviews are very essential for preserving the interests of users. To date, number of researches have been proposed in order to detect the spam reviews and to provide the genuine resources for customers and business person. However, we found few limitations in existing supervised approaches. First, most of the supervised approaches have used manual labelling of reviews into spam and non-spam. However, due to identical appearance of reviews manual labelling can not be considered as authentic. Second, the scarcity of spam reviews leads to data imbalance problem. Third, computing similarities among reviews naturally needs expensive computation. In this work, we propose a novel and robust, spam review detection system which efficiently employ following three features: (i) sentiments of review and its comments, (ii) content based factor, and (iii) rating deviation. To address the aforementioned limitations, we investigated all these features for only suspicious review list in which only those reviews have kept which received comments by peer users. The proposed system achieved the F $$_1$$ -score of 91%. The proposed system can be a great asset in spam detection system as it can be used as an stand-alone system to purify the product review datasets.