An Enhanced Topic Modeling Approach to Multiple Stance Detection

doi:https://doi.org/10.1145/3132847.3133145

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

	An Enhanced Topic Modeling Approach to Multiple Stance Detection
	Lin, Junjie1,2 ; Mao, Wenji1,2 ; Zhang, Yuhao1
	2017
会议名称	ACM International Conference on Information and Knowledge Management
页码	2167-2170
会议日期	November 6–10, 2017
会议地点	Singapore, Singapore
摘要	People often publish online texts to express their stances, which reflect the essential viewpoints they stand. Stance identification has been an important research topic in text analysis and facilitates many applications in business, public security and government decision making. Previous work on stance identification solely focuses on classifying the supportive or unsupportive attitude towards a certain topic/entity. The other important type of stance identification, multiple stance identification, was largely ignored in previous research. In contrast, multiple stance identification focuses on identifying different standpoints of multiple parties involved in online texts. In this paper, we address the problem of recognizing distinct standpoints implied in textual data. As people are inclined to discuss the topics favorable to their standpoints, topics thus can provide distinguishable information of different standpoints. We propose a topic-based method for standpoint identification. To acquire more distinguishable topics, we further enhance topic model by adding constraints on document-topic distributions. We finally conduct experimental studies on two real datasets to verify the effectiveness of our approach to multiple stance identification.
关键词	Multiple Stance Identification Topic Modeling Constrained Nonnegative Matrix Factorization
DOI	https://doi.org/10.1145/3132847.3133145
语种	英语
引用统计
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21062
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
作者单位	1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, University of Chinese Academy of Sciences 2.School of Computer and Control Engineering, University of Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Lin, Junjie,Mao, Wenji,Zhang, Yuhao. An Enhanced Topic Modeling Approach to Multiple Stance Detection[C],2017:2167-2170.

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