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Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings.
Li, Junjie1,2; Yang, Haitong3; Zong, Chengqing1,2,4
2018-08
会议名称International Conference on Computational Linguistics
会议日期2018-8
会议地点Santa Fe, New Mexico, USA
摘要

Document-level multi-aspect sentiment classification aims to predict user’s sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi-task framework is adopted. Empirical results on two real-world datasets show that HUARN achieves state-of-the-art performances.
 

收录类别其他
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/23102
专题模式识别国家重点实验室_自然语言处理
通讯作者Zong, Chengqing
作者单位1.National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
2.University of Chinese Academy of Sciences, Beijing, China
3.School of Computer, Central China Normal University, Wuhan 430079, China
4.CAS Center for Excellence in Brain Science and Intelligence Technology
推荐引用方式
GB/T 7714
Li, Junjie,Yang, Haitong,Zong, Chengqing. Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings.[C],2018.
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