Stance detection for online public opinion awareness: An overview

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 11944-11965 RESEARCH ARTICLE Stance detection for online public opinion awareness: An overview Rong Cao, Rong Cao orcid.org/0000-0002-2874-4162 Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, ChinaSearch for more papers by this authorXiangyang Luo, Corresponding Author Xiangyang Luo [email protected] Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China Correspondence Xiangyang Luo, State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China. Email: [email protected]Search for more papers by this authorYaoyi Xi, Yaoyi Xi Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, ChinaSearch for more papers by this authorYaqiong Qiao, Yaqiong Qiao Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, ChinaSearch for more papers by this author Rong Cao, Rong Cao orcid.org/0000-0002-2874-4162 Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, ChinaSearch for more papers by this authorXiangyang Luo, Corresponding Author Xiangyang Luo [email protected] Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan, China Correspondence Xiangyang Luo, State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China. Email: [email protected]Search for more papers by this authorYaoyi Xi, Yaoyi Xi Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, ChinaSearch for more papers by this authorYaqiong Qiao, Yaqiong Qiao Henan Key Laboratory of Cyberspace Situation Awareness, PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, China School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan, ChinaSearch for more papers by this author First published: 25 September 2022 https://doi.org/10.1002/int.23071Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Stance detection, which focuses on users' deep attitudes, is an important way to understand the online public opinion. This paper presents an overview of stance detection. First, we present a general framework for stance detection, and the main steps of the framework are introduced in detail. The state-of-the-art stance detection methods are categorized into three classes: feature-based methods, deep learning-based methods, and ensemble learning-based methods. Moreover, the advantages and limitations of the existing methods are analyzed. The survey findings show that hybrid-neural network-based methods are superior to the other methods. In addition, existing methods still need to pay more attention to the sentiment information, user-interaction, and attempt to merge more external knowledge to help improve the effect of stance detection. 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