Finding topic trends in digital libraries

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

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

17 Scopus citations

Abstract

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
Pages69-72
Number of pages4
DOIs
StatePublished - Nov 30 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

Other

Other2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
CountryUnited States
CityAustin, TX
Period6/15/096/19/09

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Bolelli, L., Ertekin, S., Zhou, D., & Giles, C. L. (2009). Finding topic trends in digital libraries. In JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries (pp. 69-72). (Proceedings of the ACM/IEEE Joint Conference on Digital Libraries). https://doi.org/10.1145/1555400.1555411