Tourism Informatics pp 77-90

Part of the Intelligent Systems Reference Library book series (ISRL, volume 90) | Cite as

On-Site Likelihood Identification of Tweets Using a Two-Stage Method

  • Kazutaka Shimada
  • Yurie Onitsuka
  • Shunsuke Inoue
  • Tsutomu Endo
Chapter

Abstract

The Web contains much information for the tourism, such as impressions and sentiments about sightseeing areas. Analyzing the information is a significant task for tourism informatics. A useful target resource for the analysis is information on Twitter. However, all tweets with keywords, which are related to facilities and events for tourism, might not be tourism information. In this paper, we propose a method for estimating on-site likelihood of tweets. The task is to identify whether each tweet has high on-site likelihood or not. We introduce a filtering process and a machine learning technique for the task. In addition, we apply previous and next tweets for the identification task, as context information. Experimental results show the effectiveness of the combination method and context information.

Keywords

On-site likelihood Tweet Rule Context 

References

  1. 1.
    Saito, H.: Analysis of tourism informatics on web. J. Jpn. Soc. Artif. Intell. 26(3), 234–240 (2011)MATHGoogle Scholar
  2. 2.
    Shimada, K., Inoue, S., Maeda, H., Endo, T.: Analyzing tourism information on twitter for a local city. In: Proceedings of SSNE2011, pp. 61–66 (2011)Google Scholar
  3. 3.
    Kori, H., Hattori, S., Tezuka, T., Tanaka, K.: Automatic generation of multimedia tour guide from local blogs. In: 13th International Multimedia Modeling Conference, MMM 2007, pp. 690–699 (2006)Google Scholar
  4. 4.
    Okumura, M.: Microblog mining (in Japanese). IEICE Tech. Rep. 111(427), NLC2011-59, 19–24 (2012)Google Scholar
  5. 5.
    Shimada, K., Inoue, S., Endo, T.: On-site likelihood identification of tweets for tourism information analysis. In: Proceedings of 3rd IIAI International Conference (2012)Google Scholar
  6. 6.
    Inui, K., Abe, S., Morita, H., Eguchi, M., Sumida, A., Sao, C., Hara, K., Murakami, K., Matsuyoshi, S.: Experience mining: building a large-scale database of personal experiences and opinions from web documents. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 314–321 (2008)Google Scholar
  7. 7.
    Narita, K., Mizuno, J., Inui, K.: A lexicon-based investigation of research issues in Japanese factuality analysis. In: Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013), pp. 587–595 (2013)Google Scholar
  8. 8.
    Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP) (2011)Google Scholar
  9. 9.
    Sakaki, T., Okazaki, M., Matsuo Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web (WWW2010) (2010)Google Scholar
  10. 10.
    Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 759–769 (2010)Google Scholar
  11. 11.
    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1277–1287 (2010)Google Scholar
  12. 12.
    Miyabe, M., Kita, Y., Kubo, K., Aramaki E.: Extracting aspect record related to a location from microblog (in Japanese). In: Proceedings of the 20th Annual Meeting of the Association for Natural Language Processing, pp. 420–423 (2014)Google Scholar
  13. 13.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1999)Google Scholar
  14. 14.
    Mark, H., Holmes, E., Pfahringer, G., Reutemann, B., Witten, I.H.: The Weka data mining software: an update. SIGKDD Explor. 11 (2009)Google Scholar
  15. 15.
    Shimada, K., Uehara, H., Endo, T.: A comparative study of potential-of-interest days on a sightseeing spot recommender. In: International Workshop on Sustainable Tourism Innovations and Information Systems (STIIS2014) (2014)Google Scholar
  16. 16.
    Shimada, K., Uehara, H., Endo, T.: Sightseeing location recommendation system based on collective intelligence (in Japanese). Soc. Tour. Inform. 10(1), 113–124 (2014)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kazutaka Shimada
    • 1
  • Yurie Onitsuka
    • 1
  • Shunsuke Inoue
    • 1
  • Tsutomu Endo
    • 1
  1. 1.Kyushu Institute of TechnologyIizukaJapan

Personalised recommendations