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Mapping Geotagged Tweets to Tourist Spots Considering Activity Region of Spot

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Tourism Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 90))

Abstract

We are developing a recommender system for tourist spots. The challenge is mainly to characterize tourist spots whose features change dynamically with trends, events, season, and time of day. Our method uses a one-class support vector machine (OC-SVM) to detect the regions of substantial activity near target spots on the basis of tweets and photographs that have been explicitly geotagged. A tweet is regarded as explicitly geotagged if the text includes the name of a target spot. A photograph is regarded as explicitly geotagged if the title includes the name of a target spot. To characterize the tourist spots, we focus on geotagged tweets, which are rapidly increasing on the Web. The method takes unknown geotagged tweets originating in activity regions and maps these to target spots. In addition, the method extracts features of the tourist spots on the basis of the mapped tweets. Finally, we demonstrate the effectiveness of our method through qualitative analyses using real datasets on the Kyoto area.

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Notes

  1. 1.

    https://foursquare.com/.

  2. 2.

    https://twitter.com/.

  3. 3.

    http://www.panoramio.com/.

  4. 4.

    https://developer.foursquare.com.

  5. 5.

    https://dev.twitter.com/docs/streaming-apis.

  6. 6.

    http://www.panoramio.com/api/data/api.html.

  7. 7.

    http://chasen.naist.jp/hiki/ChaSen.

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Acknowledgments

This work was supported by a Grant-in-Aid for Young Scientists (B) (23700132) from the Japan Society for the Promotion of Science (JSPS).

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Authors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, 1–1–1 Nojihigashi, Kusatsu-city, Shiga, 525–8577, Japan

    Kenta Oku & Fumio Hattori

Authors
  1. Kenta Oku
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  2. Fumio Hattori
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Corresponding author

Correspondence to Kenta Oku .

Editor information

Editors and Affiliations

  1. Graduate School of Industrial Technology, Advanced Institute of Industrial Technology, Shinagawa, Tokyo, Japan

    Tokuro Matsuo

  2. School Sustainable System Sciences, Osaka Prefecture University, Osaka, Japan

    Kiyota Hashimoto

  3. Department of Wellness Tourism, Josai International University, Chiba, Japan

    Hidekazu Iwamoto

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Oku, K., Hattori, F. (2015). Mapping Geotagged Tweets to Tourist Spots Considering Activity Region of Spot. In: Matsuo, T., Hashimoto, K., Iwamoto, H. (eds) Tourism Informatics. Intelligent Systems Reference Library, vol 90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47227-9_2

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  • DOI: https://doi.org/10.1007/978-3-662-47227-9_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47226-2

  • Online ISBN: 978-3-662-47227-9

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