Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems
Li, Qiudan1; Jin, Zhipeng1,2; Wang, Can1,2; Zeng, Daniel Dajun1,2,3; Daniel Dajun Zeng
2016-09-01
发表期刊KNOWLEDGE-BASED SYSTEMS
卷号107期号:1页码:289-300
文章类型Article
摘要Chinese microblogging is an increasingly popular social media platform. Accurately summarizing representative opinions from microblogs can increase understanding of the semantics of opinions. The unique challenges of Chinese opinion summarization in microblogging systems are automatic learning of important features and selection of representative sentences. Deep-learning methods can automatically discover multiple levels of representations from raw data instead of requiring manual engineering. However, there have been very few systematic studies on sentiment analysis of Chinese hot topics using deep-learning methods. Based on the latest deep-learning research, in this paper, we propose a convolutional neural network (CNN)-based opinion summarization method for Chinese microblogging systems. The model first applies CNN to automatically mine useful features and perform sentiment analysis; then, by making good use of the obtained sentiment features, the semantic relationships among features are computed according to a hybrid ranking function; and finally, representative opinion sentences that are semantically related to the features are extracted using Maximal Marginal Relevance, which meets "relevant novelty" requirements. Experimental results on two real-world datasets verify the efficacy of the proposed model. (C) 2016 Elsevier B.V. All rights reserved.
关键词Chinese Microblogging Systems Hot Topics Convolutional Neural Network Opinion Summarization Maximal Marginal Relevance
WOS标题词Science & Technology ; Technology
DOI10.1016/j.knosys.2016.06.017
关键词[WOS]SENTIMENT ANALYSIS
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(91224008 ; Important National Science & Technology Specific Project(2013ZX10004218) ; 61172106 ; 61402123 ; 71402177)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000380595900022
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12270
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
通讯作者Daniel Dajun Zeng
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
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GB/T 7714
Li, Qiudan,Jin, Zhipeng,Wang, Can,et al. Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems[J]. KNOWLEDGE-BASED SYSTEMS,2016,107(1):289-300.
APA Li, Qiudan,Jin, Zhipeng,Wang, Can,Zeng, Daniel Dajun,&Daniel Dajun Zeng.(2016).Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems.KNOWLEDGE-BASED SYSTEMS,107(1),289-300.
MLA Li, Qiudan,et al."Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems".KNOWLEDGE-BASED SYSTEMS 107.1(2016):289-300.
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