A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM

作者：

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.

摘要

•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.

论文关键词：Ramp-one class svm,Opinion spam,Deceptive opinion,Outlier detection

论文评审过程：Received 17 March 2020, Revised 17 July 2020, Accepted 1 September 2020, Available online 9 September 2020, Version of Record 20 October 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102381