Show simple item record

dc.contributor.advisorTeredesai, Ankur M.en_US
dc.contributor.authorShayandeh, Artaen_US
dc.date.accessioned2012-09-13T17:41:12Z
dc.date.available2012-09-13T17:41:12Z
dc.date.issued2012-09-13
dc.date.submitted2012en_US
dc.identifier.otherShayandeh_washington_0250O_10660.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/20912
dc.descriptionThesis (Master's)--University of Washington, 2012en_US
dc.description.abstractOnline social networks such as Twitter, LinkedIn, and Facebook generate tremendous amount of text and social interaction data. On one hand, the increasing amount of available information has motivated computational research in social network analysis to understand social structures. On the other hand, annotating, retrieving, and analyzing textual information generated within the social network is also crucial for many applications such as content ranking, recommendation systems, spam detection, and viral marketing. In this thesis we propose a composite probabilistic topic model for social networks which automatically learns topic (of interest) distributions for each entity in the social network using a combination of the available content (text) in social network and the structural properties of the network. The utility of our proposed modeling is to reduce the dimensionality of the data, exploit the underlying social structure and linkage property of the network while generating a more accurate topic model for the end-users of the social network. We discuss in detail the results on both the NIPS data set (papers from the Neural Information Processing Conference) and Enron Email (emails from large corporation) corpus. We present perplexity score for test documents as a basis of our experiments to evaluate the generalization performance of our model and provide evidence that relevant topics are discovered.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjecten_US
dc.subject.otherComputer scienceen_US
dc.subject.otherComputing and software systemsen_US
dc.titleAdaptive Probabilistic Topic Models for Social Networksen_US
dc.typeThesisen_US
dc.embargo.termsNo embargoen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record