Paper: Positive Unlabeled Learning for Deceptive Reviews Detection

ACL ID D14-1055
Title Positive Unlabeled Learning for Deceptive Reviews Detection
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Deceptive reviews detection has attract- ed significant attention from both business and research communities. However, due to the difficulty of human labeling need- ed for supervised learning, the problem re- mains to be highly challenging. This pa- per proposed a novel angle to the prob- lem by modeling PU (positive unlabeled) learning. A semi-supervised model, called mixing population and individual proper- ty PU learning (MPIPUL), is proposed. Firstly, some reliable negative examples are identified from the unlabeled dataset. Secondly, some representative positive ex- amples and negative examples are gener- ated based on LDA (Latent Dirichlet Al- location). Thirdly, for the remaining un- labeled examples (we call them spy ex- amples), which can not be explicitly iden- tified as positiv...