Improved Fake Reviews Detection Model Based on Vertical Ensemble Tri-Training and Active Learning

People's increasingly frequent online activity has generated a large number of reviews, whereas fake reviews can mislead users and harm their personal interests. In addition, it is not feasible to label reviews on a large scale because of the high cost of manual labeling. Therefore, to improve the detection performance by utilizing the unlabeled reviews, this article proposes a fake reviews detection model based on vertical ensemble tri-training and active learning (VETT-AL). The model combines the features of review text with the user behavior features as feature extraction. In the VETT-AL algorithm, the iterative process is divided into two parts: vertical integration within the group and horizontal integration among the groups. The intra-group integration is to integrate three original classifiers by using the previous iterative models of the classifiers. The inter-group integration is to adopt the active learning based on entropy to select the data with the highest confidence and label it, and as the result of that, the second generation classifiers are trained by the traditional process to improve the accuracy of the label. Experimental results show that the proposed model has a good classification performance.