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Industrial Conference on Data Mining

ICDM 2015: Advances in Data Mining: Applications and Theoretical Aspects pp 185–200Cite as

Making Topic Words Distribution More Accurate and Ranking Topic Significance According to the Jensen-Shannon Divergence from Background Topic

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9165))

Abstract

This paper presents a useful approach for making topic words distribution more accurate and ranking topic significance according to the Jensen-Shannon divergence from background topic as a post-procedure of LDA method. In this paper, at first we defined the term score parameter to represent topics that will suppress the correlation between different topics and make the word distribution more accurate. Then according to the correlation between different topics, we described a concrete method for determining the proper setting of the number of topics. After that we proposed a method for ranking topic significance in the order of the Jensen-Shannon divergence from background topic. As a confirmation of our proposed methods, we conducted several experiments to processing English Twitter streaming data. The results of these experiments validate that our methods work efficiently as expected.

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Notes

  1. 1.

    http://www.cs.princeton.edu/~blei/lda-c/index.html.

References

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Author information

Authors and Affiliations

  1. School of Information and Telecommunication Engineering, Tokai University, Tokyo, Japan

    Iwao Fujino & Yuko Hoshino

Authors
  1. Iwao Fujino
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  2. Yuko Hoshino
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Corresponding author

Correspondence to Iwao Fujino .

Editor information

Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Fujino, I., Hoshino, Y. (2015). Making Topic Words Distribution More Accurate and Ranking Topic Significance According to the Jensen-Shannon Divergence from Background Topic. In: Perner, P. (eds) Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science(), vol 9165. Springer, Cham. https://doi.org/10.1007/978-3-319-20910-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-20910-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20909-8

  • Online ISBN: 978-3-319-20910-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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