融合内容及行为的虚假评论检测方法研究

发布时间：2018-08-15 19:04

【摘要】：随着互联网的发展,特别是电子商务的飞速发展,越来越多的消费者青睐于网上购物,消费者越来越容易针对自己购买的产品发表评论,这些产品评论信息为厂家以及潜在消费者提供了宝贵的信息资源。由于存在某些利益关系,其中可能存在一些不实或虚假的内容,这些虚假评论在一定程度上影响了评论信息的参考价值,从而误导消费者,因此检测虚假评论尤为重要。最基本的评论信息是评论的内容信息,对评论内容信息进行挖掘,利用评论内容信息对虚假评论进行检测有着极其重要的意义；此外,对评论者行为进行挖掘,通过发现异常的行为模式来识别虚假评论也有着重要的作用。本文以产品和服务评论为主,围绕基于评论内容的虚假评论检测、基于评论者行为的虚假评论检测、融合评论内容及评论者行为这两类特征来检测虚假评论等关键问题开展研究,主要完成了以下研究工作： (1)提出了一种基于评论内容的虚假评论检测方法。该方法首先构建基于情感依赖的评论主题-对立情感依赖模型(topic-opposite sentiment dependency model, TOSDM),利用该模型提取评论的主题信息以及主题对应的情感信息；然后,结合评论的主题以及情感信息,分析并提取6维评论内容特征；最后,利用这些评论内容特征,采用有监督学习的分类器对虚假评论进行检测。 (2)提出了一种基于评论者行为的虚假评论检测方法。该方法首先根据评论数据选取10维反映评论者行为的特征,并对每维特征进行归一化处理；然后,根据每一条评论的特征构建聚类矩阵,利用F统计量对K均值算法进行改进,实现评论数据的自适应聚类；最后,计算每个簇偏离整个评论数据集的程度,根据阈值确定异常簇,从而实现虚假评论检测。 (3)提出了一种融合评论内容及评论者行为的半监督虚假评论检测方法。该方法首先对评论的内容特征以及评论者的行为特征进行提取,然后借助Co-Training的半监督学习思想,将这两类特征看作相互独立的视图,利用这两类独立的特征分别建立分类器,挑选置信度高的未标注样本,最后使用这些挑选出的样本更新训练模型,改善分类器效果。 (4)设计并实现了虚假评论检测原型系统,为进一步研究虚假评论的检测方法提供了便利。
[Abstract]:With the development of the Internet, especially the rapid development of electronic commerce, more and more consumers prefer to shop online, and it is more and more easy for consumers to comment on the products they buy. These product reviews provide valuable information resources for manufacturers and potential consumers. Due to the existence of some interest relations, there may be some false or false content, these false comments to a certain extent affect the reference value of comment information, thus misleading consumers, so it is particularly important to detect false comments. The most basic comment information is the content information of the comment. It is very important to mine the content information of the comment and detect the false comment by using the content information of the comment; in addition, it is very important to mine the behavior of the reviewer. Identifying false comments by discovering abnormal behavior patterns also plays an important role. This paper focuses on product and service reviews, focusing on the detection of false comments based on the content of comments, and the detection of false comments based on the behavior of reviewers. The key issues of detecting false comments such as comment content and reviewer behavior are studied. The main works are as follows: (1) A method of false comment detection based on comment content is proposed. In this method, a motif based on affective dependency is constructed, which is used by topic-opposite sentiment dependency model, TOSDM), to extract the subject information of comments and their corresponding emotional information, and then combines the subject and emotional information of comments. Finally, a supervised learning classifier is used to detect false comments. (2) A method of false comment detection based on reviewer's behavior is proposed. The method firstly selects 10 dimensions to reflect the behavior of the reviewer according to the comment data, and normalizes the feature of each dimension. Then, the clustering matrix is constructed according to the characteristics of each comment, and the K-means algorithm is improved by using F statistics. Finally, the degree of each cluster deviating from the whole comment data set is calculated, and the abnormal cluster is determined according to the threshold. Thus, the detection of false comments is realized. (3) A semi-supervised detection method of false comments is proposed, which combines the content of comments with the behavior of the reviewers. The method firstly extracts the content features of comments and the behavioral features of reviewers. Then, with the help of Co-Training 's semi-supervised learning idea, the two kinds of features are regarded as independent views, and the classifiers are constructed using the two independent features. The unlabeled samples with high confidence are selected. Finally, the training model is updated with these selected samples to improve the classifier effect. (4) A prototype system of false comment detection is designed and implemented. It is convenient to further study the detection method of false comment.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.08

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