基于双层堆叠分类模型的水军评论检测

对于水军评论检测问题,已有方法在提取用户行为关系以及通过神经网络提取特征时复杂度过大,同时由于网络评论属于短文本类,其书写的不规范会导致训练过程中文本特征提取困难;另外,已有方法对数据集不平衡分布情况考虑不足。为此,提出了一种基于双层堆叠分类模型的水军评论检测方法。首先通过三元组形式构造矩阵表示用户间关系,并通过主成分分析得到低维用户关系表示,以此刻画用户在评论数据中的行为差异并且降低计算的复杂度;然后,通过评论的段落向量表示以及计算离散型特征(包括文本相似度、信息熵等)解决文本特征难以提取的问题;最后将三者相联结作为融合文本与行为特征的整体特征表示。利用集成学习的方法构造双层堆叠分类模型对评论分类,以提升模型在非平衡数据集下的检测性能。实验采用Yelp2013评论数据集,结果表明,与目前最好的基准方法对比, F₁值提高了1.7%~5.2%,在非平衡数据集中提升尤为明显。

Review spam detection based on the two-level stacking classification model

For the issue of review spam detection, on the one hand, the time and space complexity of existing methods is high when extracting user behavior relationships and training neural network. On the other hand, the non-standard writing format of E-commercial reviews leads to the indistinct contextual features and most experiment did not consider the effect of the imbalance of data. Therefore, we propose a method for review spam detection based on a two-level stacking classification model. In the method, the relationship between users and products is represented by a triplet. In order to characterize user's behavior and reduce complexity, low-dimensional feature representations are obtained by the principal component analysis. Then, the extracted paragraphs vector representation, information entropy and text similarity is represented as discrete feature to avoid indistinct of contextual features. Finally, the three connections are taken as the overall features combining text and behavioral features. These features are regarded as the input of the two-level stacking classification model in order to improve performance in unbalanced dataset. We conducted experiments in the Yelp 2013 dataset. Experimental results show the F₁ value of our proposed method is 1.7%—5.2% better than the state-of-the-art method. What's more, the classification performance is significantly improved in the unbalanced dataset.