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 JJ(李俊杰)1,2; Yang HT(杨海彤)3; Zong CQ(宗成庆)1,2,4
2018
出版者International Conference on Computational Linguistics
会议名称the 27th International Conference on Computational Linguistics (COLING)
出版地American
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.
 

文献类型会议录
条目标识符http://ir.ia.ac.cn/handle/173211/23067
专题模式识别国家重点实验室_自然语言处理
通讯作者Zong CQ(宗成庆)
作者单位1.中国科学院自动化研究所模式识别国家重点实验室
2.中国科学院大学
3.华中师范大学
4.中国科学院脑科学与智能技术卓越创新中心
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Li JJ,Yang HT,Zong CQ. Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings[C].American:International Conference on Computational Linguistics,2018.
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