Finding Topic Trends in Digital Libraries

Creative Commons License

Bolelli L., Ertekin Ş. , Zhou D., Giles C. L.

9th Annual International ACM/IEEE Joint Conference on Digital Libraries, Texas, United States Of America, 15 - 19 June 2009, pp.69-72 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1145/1555400.1555411
  • City: Texas
  • Country: United States Of America
  • Page Numbers: pp.69-72


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 Cite Seer 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.