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
Source PublicationKNOWLEDGE-BASED SYSTEMS
2016-09-01
Volume107Issue:1Pages:289-300
SubtypeArticle
AbstractChinese 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.
KeywordChinese Microblogging Systems Hot Topics Convolutional Neural Network Opinion Summarization Maximal Marginal Relevance
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.knosys.2016.06.017
WOS KeywordSENTIMENT ANALYSIS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91224008 ; Important National Science & Technology Specific Project(2013ZX10004218) ; 61172106 ; 61402123 ; 71402177)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000380595900022
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12270
Collection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
Corresponding AuthorDaniel Dajun Zeng
Affiliation1.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
Recommended Citation
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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