中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
An Enhanced Topic Modeling Approach to Multiple Stance Detection

文献类型:会议论文

作者Lin, Junjie1,2; Mao, Wenji1,2; Zhang, Yuhao1
出版日期2017
会议日期November 6–10, 2017
会议地点Singapore, Singapore
关键词Multiple Stance Identification Topic Modeling Constrained Nonnegative Matrix Factorization
DOIhttps://doi.org/10.1145/3132847.3133145
页码2167-2170
英文摘要
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.
语种英语
源URL[http://ir.ia.ac.cn/handle/173211/21062]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
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]. 见:. Singapore, Singapore. November 6–10, 2017.

入库方式: OAI收割

来源:自动化研究所

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