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2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Enhancing Community Discovery and Characterization in VCoP Using Topic Models
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
The identification of communities in social networks is a common problem that researchers have been dealing using network analysis properties. However, in environments where community members are connected by digital documents, most researchers have either emphasize to solve the community discovery problem computing structural properties of networks, ignoring the underlying semantic information from digital documents. In this paper, we propose a novel approach to combine traditional network analysis methods for community detection with text mining techniques. This way, extracted communities can be labeled according to latent semantic information within documents, called topics. Our proposal was evaluated in Plexilandia, a virtual community of practice with more than 2,500 members and 9 years of commentaries.
Index Terms:
Web Intelligence, Community Discovery, Social Network Analysis, Text Mining, Latent Dirichlet Allocation
Citation:
Lautaro Cuadra, Sebasti´n A. Ríos, Gaston L'Huillier, "Enhancing Community Discovery and Characterization in VCoP Using Topic Models," wi-iat, vol. 3, pp.326-329, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
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