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JCDL
2009
ACM

Finding topic trends in digital libraries

12 years 4 months ago
Finding topic trends in digital libraries
We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the generative process. The document collection is divided into time segments where the discovered topics in each segment is propagated to influence the topic discovery in the subsequent time segments. We conduct experiments on the collection of academic papers from CiteSeer repository. We augment the text corpus with the addition of user queries and tags and integrate the citation graph to boost the weight of the topical terms. The experiment results show that segmented topic model can effectively detect distinct topics and their evolution over time. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications— data mining General Terms Algorithms, Design, Experimentation
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where JCDL
Authors Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
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