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International Conference on Intelligent Decision Technologies

IDT 2017: Intelligent Decision Technologies pp 587–597Cite as

Mining Historical Social Issues

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 39))

Abstract

This paper presents a framework for identifying human histories that are similar to a modern social issue specified by a learner. From the historical data, the learner can study how people in history tried to resolve social issues and what results they achieved. This can help the learner consider how to resolve the modern social issue. To identify issues in history similar to a given modern issue, our framework uses the characteristics and explanation of the specified modern issue in two techniques: clustering and classification. These techniques identify the similarity between historical and the modern issues by using matrix operation and text classification. We implemented our proposed framework and evaluated it in terms of analysis time. Experimental results proved that our framework has practical usage with an analysis time of only about 0.7 s.

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Notes

  1. 1.

    CD-Mainichi Newspapers 2012 data, Nichigai Associates, Inc., 2012 (Japanese).

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 26750076.

Author information

Authors and Affiliations

  1. Tokyo University of Science, Tokyo, Japan

    Yasunobu Sumikawa

  2. The University of Tokyo, Tokyo, Japan

    Ryohei Ikejiri

Authors
  1. Yasunobu Sumikawa
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  2. Ryohei Ikejiri
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Corresponding author

Correspondence to Yasunobu Sumikawa .

Editor information

Editors and Affiliations

  1. FCT, Universidade Nova de Lisboa, Caparica, Portugal

    Rui Neves-Silva

  2. Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

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© 2015 Springer International Publishing Switzerland

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Sumikawa, Y., Ikejiri, R. (2015). Mining Historical Social Issues. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_50

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

  • eBook Packages: EngineeringEngineering (R0)

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