Conference Proceeding Article
Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot issues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discovery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: viewpoint specific topic preference, user identity and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering posts based on viewpoints and clustering users with different viewpoints.
discussion forum posts, user identity, user interaction, user viewpoints, model
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Data Management and Analytics
Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics-Human Language Technologies 2013, 9-14 June, Atlanta
Association for Computational Linguistics
City or Country
QIU, Minghui and JIANG, Jing.
A Latent Variable Model for Viewpoint Discovery from Threaded Forum Posts. (2013). Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics-Human Language Technologies 2013, 9-14 June, Atlanta. 1031-1040. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1890
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