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
DOIhttps://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
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
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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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