A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM. Issue 6 (November 2020) - Tian, Yingjie; Mirzabagheri, Mahboubeh; Tirandazi, Peyman; Bamakan, Seyed Mojtaba Hosseini
Highlights: Ramp-one OC-SVM as a non-convex semi-supervised technique. The lack of labeled data for the deceptive opinions is addressed by R-OCSVM. The effect of noises and outliers in the training set is diminished by ramp loss function. The challenges and the future direction in opinion spam detection is discussed. Abstract: Nowadays, e-commerce has become a part of our daily life in such a way that people's decision for buying products or choosing services highly depends on comments, reviews, and rates, which are posted on related businesses' website and other social media. Because of the importance and prevalence of these sources of information, fraudsters are tempted to use fraudulently opinion sharing platforms in order to promote or to discredit some target products or services. Although a wide range of approaches have been proposed to address this problem and to help distinguish between the deceptive or fraudulent opinions from the trustful ones, this is still a challenging problem. Lack of well-defined deceptive data samples or insufficiency of spam review instances in the training sets cause supervised techniques facing an imbalanced classes problem. Furthermore, even in the golden normal opinion datasets, there is a possibility of the presence of some abnormal records or outliers. To deal with these two issues, we propose a robust and non-convex semi-supervised algorithm called "Ramp One-Class SVM". In the proposed method, one-class SVM is adopted to handle the lack of labeled data for the deceptive opinions and by taking the advantages of non-convex properties of the Ramp loss function, we eliminate the effects of outliers and non-review opinions. The performance of the proposed method is evaluated by an artificial dataset and two real datasets including Ott and Yelp crowdsourced datasets. The results show the superiority of our method by achieving an accuracy of 92.13% and 74.37% for Ott and Yelp crowdsourced datasets, respectively. The obtained results also reveal the effectiveness of the proposed model in terms of precision, recall, generalization power, and robustness to the outliers.