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Topic Modeling for Web Community Discovery

Topic Modeling for Web Community Discovery

Kulwadee Somboonviwat
Copyright: © 2013 |Pages: 18
ISBN13: 9781466628069|ISBN10: 1466628065|EISBN13: 9781466628076
DOI: 10.4018/978-1-4666-2806-9.ch005
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MLA

Somboonviwat, Kulwadee. "Topic Modeling for Web Community Discovery." Social Media Mining and Social Network Analysis: Emerging Research, edited by Guandong Xu and Lin Li, IGI Global, 2013, pp. 72-89. https://doi.org/10.4018/978-1-4666-2806-9.ch005

APA

Somboonviwat, K. (2013). Topic Modeling for Web Community Discovery. In G. Xu & L. Li (Eds.), Social Media Mining and Social Network Analysis: Emerging Research (pp. 72-89). IGI Global. https://doi.org/10.4018/978-1-4666-2806-9.ch005

Chicago

Somboonviwat, Kulwadee. "Topic Modeling for Web Community Discovery." In Social Media Mining and Social Network Analysis: Emerging Research, edited by Guandong Xu and Lin Li, 72-89. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2806-9.ch005

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Abstract

The proliferation of the Web has led to the simultaneous explosive growth of both textual and link information. Many techniques have been developed to cope with this information explosion phenomenon. Early efforts include the development of non-Bayesian Web community discovery methods that exploit only link information to identify groups of topical coherent Web pages. Most non-Bayesian methods produce hard clustering results and cannot provide semantic interpretation. Recently, there has been growing interest in applying Bayesian-based approaches to discovering Web community. The Bayesian approaches for Web community discovery possess many good characteristics such as soft clustering results and ability to provide semantic interpretation of the extracted communities. This chapter presents a systematic survey and discussions of non-Bayesian and Bayesian-based approaches to the Web community discovery problem.

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