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ChronoSAGE: Diversifying Topic Modeling Chronologically

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8485))

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Abstract

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.

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References

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Author information

Authors and Affiliations

  1. Nagasaki University, 1-14 Bunkyo-machi, Nagasaki, 8528521, Japan

    Tomonari Masada

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 1018430, Japan

    Atsuhiro Takasu

Authors
  1. Tomonari Masada
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  2. Atsuhiro Takasu
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Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, 50 S. Central Campus Drive, 84112, Salt Lake City,, UT, USA

    Feifei Li

  2. Department of Computer Science, Tsinghua University, 100084, Beijing, China

    Guoliang Li

  3. POSTECH, Republic of Korea

    Seung-won Hwang

  4. Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering,, Shanghai Jiao Tong University, China

    Bin Yao

  5. Advanced Digital Sciences Center (ADSC), 138632, Singapore, Singapore

    Zhenjie Zhang

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© 2014 Springer International Publishing Switzerland

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Masada, T., Takasu, A. (2014). ChronoSAGE: Diversifying Topic Modeling Chronologically. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_51

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  • DOI: https://doi.org/10.1007/978-3-319-08010-9_51

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08009-3

  • Online ISBN: 978-3-319-08010-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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