ChronoSAGE: Diversifying Topic Modeling Chronologically

  • Tomonari Masada
  • Atsuhiro Takasu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8485)


This paper provides an application of sparse additive generative models (SAGE) for temporal topic analysis. In our model, called ChronoSAGE, topic modeling results are diversified chronologically by using document timestamps. That is, word tokens are generated not only in a topic-specific manner, but also in a time-specific manner. We firstly compare ChronoSAGE with latent Dirichlet allocation (LDA) in terms of pointwise mutual information to show its practical effectiveness. We secondly give an example of time-differentiated topics, obtained by ChronoSAGE as word lists, to show its usefulness in trend detection.


Word Pair Word List External Evaluation Word Token Word Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tomonari Masada
    • 1
  • Atsuhiro Takasu
    • 2
  1. 1.Nagasaki UniversityNagasakiJapan
  2. 2.National Institute of InformaticsChiyoda-kuJapan

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