Finding topic trends in digital libraries

Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationJCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
Number of pages4
StatePublished - 2009
Event2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09 - Austin, TX, United States
Duration: Jun 15 2009Jun 19 2009

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996


Other2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
Country/TerritoryUnited States
CityAustin, TX

All Science Journal Classification (ASJC) codes

  • General Engineering


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