Knowledge Commons of Institute of Automation,CAS
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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
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