An Enhanced Topic Modeling Approach to Multiple Stance Identification

Junjie Lin, Wenji Mao, Yuhao Zhang

Abstract: 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.