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.

中文翻译：

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基于Vertical Ensemble Tri-Training和主动学习的改进假评论检测模型

人们日益频繁的在线活动产生了大量的评论，而虚假评论会误导用户并损害他们的个人利益。此外，由于人工标注成本高昂，大规模标注评论也不可行。因此，为了利用未标记的评论来提高检测性能，本文提出了一种基于垂直集成三训练和主动学习（VETT-AL）的虚假评论检测模型。该模型将评论文本的特征与用户行为特征相结合作为特征提取。在VETT-AL算法中，迭代过程分为两部分：群内垂直整合和群间水平整合。组内集成是利用分类器之前的迭代模型对三个原始分类器进行集成。组间集成是采用基于熵的主动学习，选择置信度最高的数据进行标注，通过传统流程训练第二代分类器，提高标注的准确率. 实验结果表明，该模型具有良好的分类性能。