支持跨领域的中文虚假评论识别方法

Support for Cross-Domain Methods of Identifying Fake Comments of Chinese

【目的】在多领域数据集的基础上,构建一种基于评论文本深层词关系语义信息提取的支持跨领域的中文虚假评论识别模型CFEE,解决传统识别方法较少考虑中文评论文本中存在不同领域数据差异性和领域虚假评论数据隐藏性的问题。【方法】提出11条虚假评论数据集建立规则,建立多领域数据集;构建CFEE模型跨领域识别中文虚假评论,其主要功能为基于ERNIE预训练模型提取文本深层语义信息、基于评论文本情感属性识别评论隐藏性、基于卷积神经网络将文本信息投射到词关系维度、基于神经网络融合特征实现分类。【结果】CFEE模型在多领域中文虚假评论数据集上的F1值为91.52%,在手机、食品、服装、家电等单领域数据集上的F1值分别为85.71%、79.59%、85.71%、85.00%,效果均显著优于现有模型。【局限】存在人工标注的主观性。【结论】本文所提识别方法能够有效地跨领域识别中文虚假评论。

[Objective]This paper constructs a cross-domain Chinese fake review identification model(CFEE)for multi-domain datasets.It extracts the semantic information of the comment texts and addresses the problems of traditional recognition models.[Methods]First,we established 11 rules for constructing fake review datasets and created a multi-domain dataset.Then,we designed the CFEE model to identify Chinese fake comments across domains.Third,it extracted the deep semantic information with the ERNIE pre-training model.The model identified the hidden comments based on the texts’emotional attributes.Finally,it projected the text information to the word relation dimension with the convolutional neural network and realized classification based on features of neural network fusion.[Results]The CFEE model’s F1 value reached 91.52%on the multi-domain Chinese fake comment datasets.The model’s F1 values were 85.71%,79.59%,85.71%,and 85.00%on single-domain datasets for mobile phones,food,clothing,and household appliances,respectively.It outperformed the existing models significantly.[Limitations]There is subjectivity in the manual annotation.[Conclusions]The proposed method can effectively identify Chinese fake reviews across domains.