Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems

doi:10.1016/j.knosys.2016.06.017

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	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
发表期刊	KNOWLEDGE-BASED SYSTEMS
	2016-09-01
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
DOI	10.1016/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	被引频次：52[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12270
专题	多模态人工智能系统全国重点实验室_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
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, 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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