题　目：TM-OKC: An Unsupervised Topic Model for Text in Online Knowledge Communities

嘉　宾：Dongcheng ZHANG, Post-doctoral fellow, Emory University

主持人：魏煊  助理教授  77779193永利官网

时　间：2023年12月01日（周五）14:00-15:30

地　点：腾讯会议

(校内师生如需会议号和密码，请发送电邮至xuziqing@sjtu.edu.cn获取)

内容简介:

Online knowledge communities (OKCs), such as question-and-answer sites, have become increasingly popular venues for knowledge sharing. Accordingly, it is necessary for researchers and practitioners to develop effective and efficient text analysis tools to understand the massive amount of user-generated content (UGC) on OKCs. Unsupervised topic modeling has been widely adopted to extract human-interpretable latent topics embedded in texts. These identified topics can be further used in subsequent analysis and managerial practices. However, existing generic topic models that assume documents are independent are inappropriate for analyzing OKCs where structural relationships exist between questions and answers. Thus, a new method is needed to fill this research gap. In this study we propose a new topic model specifically designed for the text in OKCs.

We make three primary contributions to the research on topic modeling in this context. First, we build a general and flexible Bayesian framework to explicitly model structural and temporal dependencies among texts. Second, we statistically demonstrate the approximate model inference using mean-field and coordinate ascent algorithms. Third, we showcase the practical value and relative merit of our method via a specific downstream task (i.e., user profiling). The proposed model is illustrated using two real-world datasets from well-known OKCs (i.e., Stack Exchange and Quora), and extensive experiments demonstrate its superiority over several cutting-edge benchmarks.

演讲人简介：

Dongcheng Zhang is a post-doctoral fellow at Goizueta Business School of Emory University. Prior to joining Goizueta, he received his Ph.D. in Management Science and Engineering, BE in Engineering, and BA in Management from Tsinghua University. His research focuses on developing and applying machine learning algorithms, statistical methods, and analytical models to improve decision-making in digital marketing and management information systems. In particular, he is interested in developing interpretable and theory-driven machine learning/deep learning algorithms for substantive business problems (e.g., text mining, consumer choices, and causal inference).

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