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A SVM Applied Text Categorization of Academia-Industry Collaborative Research and Development Documents on the Web

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Analysis and Modeling of Complex Data in Behavioral and Social Sciences

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Abstract

A method of automatically extracting Japanese documents describing University-Industry (U-I) relations from the Web is proposed. The proposed method consists of Japanese text processing and support vector machine (SVM) classification. The SVM feature selections were customized for U-I relations documents. The strongest experimental result was 79.95 of accuracy and 81.17 of f-measure.

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Notes

  1. 1.

    http://mecab.googlecode.com/svn/trunk/mecab/doc/index.html.

  2. 2.

    http://svmlight.joachims.org/.

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Author information

Authors and Affiliations

  1. National Institute of Informatics, Tokyo, 101-8430, Japan

    Kei Kurakawa & Yuan Sun

  2. GMO Research, Tokyo, 150-8512, Japan

    Nagayoshi Yamashita

  3. The Institute of Statistical Mathematics, Tokyo, 190-8562, Japan

    Yasumasa Baba

Authors
  1. Kei Kurakawa
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  2. Yuan Sun
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  3. Nagayoshi Yamashita
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  4. Yasumasa Baba
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Corresponding author

Correspondence to Kei Kurakawa .

Editor information

Editors and Affiliations

  1. Department of Statistical Science, University of Rome "La Sapienza", Rome, Italy

    Donatella Vicari

  2. and Information Sciences, Tama University Graduate School of Management, Tokyo, Japan

    Akinori Okada

  3. Department of Political Science, University of Naples "Federico II", Naples, Italy

    Giancarlo Ragozini

  4. Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany

    Claus Weihs

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© 2014 Springer International Publishing Switzerland

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Kurakawa, K., Sun, Y., Yamashita, N., Baba, Y. (2014). A SVM Applied Text Categorization of Academia-Industry Collaborative Research and Development Documents on the Web. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_19

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