CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
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
Conference NameInternational Conference on Computational Linguistics
Conference Date2018-8
Conference PlaceSanta Fe, New Mexico, USA
Abstract

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.
 

Indexed By其他
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23102
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorZong, Chengqing
Affiliation1.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
Recommended Citation
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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