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A deceptive detection model based on topic, sentiment, and sentence structure information
Applied Intelligence ( IF 5.3 ) Pub Date : 2020-07-04 , DOI: 10.1007/s10489-020-01779-0
Xiaodong Du , Ruiqi Zhu , Fuqiang Zhao , Fangzhou Zhao , Ping Han , Zhengyu Zhu

Deceptive reviews on Web are a common phenomenon and how to detect them has a very important impact on products, services, and even business policies. In order to filter out deceptive reviews more accurately, a new model called Sentence Joint Topic Sentiment Model (SJTSM) is presented in this paper, which incorporates the sentence structure of reviews and the sentiment label information of words based on Latent Dirichlet Allocation (LDA) model to extract the review features. The proposed model employs Gibbs algorithm to estimate the maximum likelihood parameters and takes the vector of topic-sentiment distribution as the review features. Then a voting system of multiple-classifier, which takes the extracted review feature vector as its input is designed to realize the classification of deceptive review detection. The comparative experiments on different public datasets with other existing methods based on LDA model show that the new classifying system based on SJTSM model can achieve more satisfying classification results on deceptive review detection.



中文翻译:

基于主题,情感和句子结构信息的欺骗性检测模型

Web上的欺骗性评论是一种普遍现象,如何检测到它们会对产品,服务甚至业务策略产生非常重要的影响。为了更准确地过滤出欺骗性评论,本文提出了一种新的模型,称为句子联合主题情感模型(SJTSM),该模型结合了评论的句子结构和基于潜在狄利克雷分配(LDA)的单词情感标签信息模型以提取评论功能。提出的模型采用吉布斯算法估计最大似然参数,并以主题情感分布矢量作为评价特征。然后设计了以提取的评论特征向量为输入的多分类器投票系统,实现了欺骗性评论检测的分类。

更新日期:2020-07-05
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