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Inferring Tourist Behavior and Purposes of a Twitter User

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Book cover Trends in Artificial Intelligence: PRICAI 2016 Workshops (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10004))

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Abstract

The importance of tourism information such as tourism purposes and tourist behavior continues to increase. However, obtaining precise tourist information such as the tourist destination and tourism period is difficult, as is applying that information to actual tourism marketing. We propose a method to classify Twitter user into tourist behavior and tourism purposes, extracting related information from Twitter posts. Our experiments demonstrated a 0.65 F-score for multi-class classification, showing accuracy for inferring tourist behavior and tourism purposes.

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Notes

  1. 1.

    http://www.mlit.go.jp/kankocho/siryou/toukei/syouhityousa.html.

  2. 2.

    http://www.mlit.go.jp/kankocho/siryou/toukei/shouhidoukou.html.

  3. 3.

    https://dev.twitter.com/overview/documentation.

  4. 4.

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

  5. 5.

    https://github.com/neologd/mecab-ipadic-neologd.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 16K00157, 16K16158, and Tokyo Metropolitan University Grant-in-Aid for Research on Priority Areas “Research on social big data”. We are grateful for the assistance by Yoshiyuki Shoji.

Author information

Authors and Affiliations

  1. Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan

    Yuya Nozawa, Masaki Endo & Hiroshi Ishikawa

  2. Division of Core Manufacturing, Polytechnic University, Tokyo, Japan

    Masaki Endo

  3. National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Yo Ehara

  4. Department of Information Engineering, National Institute of Technology, Oita College, Oita, Japan

    Masaharu Hirota

  5. Faculty of Informatics, Shizuoka University, Hamamatsu, Japan

    Syohei Yokoyama

Authors
  1. Yuya Nozawa
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  2. Masaki Endo
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  3. Yo Ehara
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  4. Masaharu Hirota
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  5. Syohei Yokoyama
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  6. Hiroshi Ishikawa
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Corresponding author

Correspondence to Yuya Nozawa .

Editor information

Editors and Affiliations

  1. ISIR, Osaka University, Ibaraki, Osaka, Japan

    Masayuki Numao

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Language and Semantic Technology Lab, Pathum Thani, Thailand

    Thepchai Supnithi

  4. Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Mahasak Ketcham

  5. Mahidol University, Bangkok, Thailand

    Narit Hnoohom

  6. Media Technology, King Mongkut's University of Technology Thonburi, Thonburi, Thailand

    Patiyuth Pramkeaw

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Nozawa, Y., Endo, M., Ehara, Y., Hirota, M., Yokoyama, S., Ishikawa, H. (2017). Inferring Tourist Behavior and Purposes of a Twitter User. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-60675-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60674-3

  • Online ISBN: 978-3-319-60675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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