A Topic Enhanced Approach to Detecting Multiple Standpoints in Web Texts
Junjie Lin1,2; Qingchao Kong1,2; Wenji Mao1,2; Lei Wang1
Source PublicationInformation Sciences
2019
Volume501Issue:0Pages:483-494
Abstract

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.

KeywordMultiple Standpoint Detectiontopic Modeltopic Enhanced Approachadaptive Parameter Determination
DOIhttps://doi.org/10.1016/j.ins.2019.05.068
URL查看原文
Indexed BySCI
Language英语
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26085
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 AuthorQingchao Kong
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing 101408, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Junjie Lin,Qingchao Kong,Wenji Mao,et al. A Topic Enhanced Approach to Detecting Multiple Standpoints in Web Texts[J]. Information Sciences,2019,501(0):483-494.
APA Junjie Lin,Qingchao Kong,Wenji Mao,&Lei Wang.(2019).A Topic Enhanced Approach to Detecting Multiple Standpoints in Web Texts.Information Sciences,501(0),483-494.
MLA Junjie Lin,et al."A Topic Enhanced Approach to Detecting Multiple Standpoints in Web Texts".Information Sciences 501.0(2019):483-494.
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