Learning to Represent Review with Tensor Decomposition for Spam Detection

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	Learning to Represent Review with Tensor Decomposition for Spam Detection
	Wang Xuepeng; Liu Kang; He Shizhu; Zhao Jun
	2016-11
会议日期	November 1-5, 2016
会议地点	Austin, Texas, USA
关键词	Represent Learning Tensor Decomposition Spam Detection
英文摘要	Review spam detection is a key task in opinion mining. To accomplish this type of detection, previous work has focused mainly on effectively representing fake and non-fake reviews with discriminative features, which are discovered or elaborately designed by experts or developers. This paper proposes a novel review spam detection method that learns the representation of reviews automatically instead of heavily relying on experts’ knowledge in a data-driven manner. More specifically, according to 11 relations (generated automatically from two basic patterns) between reviewers and products, we employ tensor decomposition to learn the embeddings of the reviewers and products in a vector space. We collect relations between any two entities (reviewers and products), which results in much useful and global information. We concatenate the review text, the embeddings of the reviewer and the reviewed product as the representation of a review. Based on such representations, the classifier could identify the opinion spam more precisely. Experimental results on an open Yelp dataset show that our method could effectively enhance the spam detection accuracy compared with the stateof- the-art methods.
会议录	Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/41073]
专题	模式识别国家重点实验室_自然语言处理
通讯作者	Liu Kang
推荐引用方式 GB/T 7714	Wang Xuepeng,Liu Kang,He Shizhu,et al. Learning to Represent Review with Tensor Decomposition for Spam Detection[C]. 见:. Austin, Texas, USA. November 1-5, 2016.