A topic enhanced approach to detecting multiple standpoints in web texts | |
Lin, Junjie1,2; Kong, Qingchao1,2; Mao, Wenji1,2; Wang, Lei1,2 | |
发表期刊 | INFORMATION SCIENCES |
ISSN | 0020-0255 |
2019-10-01 | |
卷号 | 501页码:483-494 |
通讯作者 | Kong, Qingchao(qingchao.kong@ia.ac.cn) |
摘要 | Internet has become the most popular platform for people to exchange opinions and express stances. The stances implied in web texts indicate people's fundamental beliefs and viewpoints. Understanding the stances people take is beneficial and critical for many security and business related applications, such as policy design, emergency response and marketing management. Most previous work on stance detection focuses on identifying the supportive or unsupportive attitudes towards a specific target. However, another important type of stance detection, i.e. multiple standpoint detection, has been largely ignored. Multiple standpoint detection aims to identify the distinct standpoints people hold among multiple parties, which reflects people's intrinsic values and judgments. When expressing standpoints, people tend to discuss diverse topics, and the word usage in the topics of different standpoints often varies a lot. As topics can provide latent information for identifying various standpoints, in this paper, we propose a topic-based approach to detecting multiple standpoints in Web texts, by enhancing generative classification model as well as feature representation of texts. In addition, we develop an adaptive process to determine parameter values in our approach automatically. Experimental studies on several real-world datasets verify the effectiveness of our proposed approach in detecting multiple standpoints. (C) 2019 Elsevier Inc. All rights reserved. |
关键词 | Multiple standpoint detection Topic model Topic enhanced approach Adaptive parameter determination |
DOI | 10.1016/j.ins.2019.05.068 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2016QY02D0305] ; National Natural Science Foundation of China[71702181] ; National Natural Science Foundation of China[11832001] ; National Natural Science Foundation of China[71621002] ; National Science and Technology Major Project[2018ZX10201001] ; Early Career Development Award of SKLMCCS[20180208] ; National Key R&D Program of China[2016QY02D0305] ; National Natural Science Foundation of China[71702181] ; National Natural Science Foundation of China[11832001] ; National Natural Science Foundation of China[71621002] ; National Science and Technology Major Project[2018ZX10201001] ; Early Career Development Award of SKLMCCS[20180208] |
项目资助者 | National Key R&D Program of China ; National Natural Science Foundation of China ; National Science and Technology Major Project ; Early Career Development Award of SKLMCCS |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems |
WOS记录号 | WOS:000480663900029 |
出版者 | ELSEVIER SCIENCE INC |
七大方向——子方向分类 | 自然语言处理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26085 |
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 | Kong, Qingchao |
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 101408, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Lin, Junjie,Kong, Qingchao,Mao, Wenji,et al. A topic enhanced approach to detecting multiple standpoints in web texts[J]. INFORMATION SCIENCES,2019,501:483-494. |
APA | Lin, Junjie,Kong, Qingchao,Mao, Wenji,&Wang, Lei.(2019).A topic enhanced approach to detecting multiple standpoints in web texts.INFORMATION SCIENCES,501,483-494. |
MLA | Lin, Junjie,et al."A topic enhanced approach to detecting multiple standpoints in web texts".INFORMATION SCIENCES 501(2019):483-494. |
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