Tourist Information Extraction Method from Tweets Without Tourist Spot Names for Tourist Information Visualization System

  • Sayuri WatanabeEmail author
  • Takashi Yoshino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10397)


We developed a system to extract tourist information from the web. However, insufficient tourist information is often provided from Twitter. We believe that previous methods could not consider tweets about tourist spots that did not contain the tourist spot name. In this study, we propose a tourist information extraction method from tweets without tourist spot names. In our experiment, we evaluated whether tourist information was contained in tweets before and after tweets containing the tourist spot names, tweets of followers of the user who tweeted tourist spot names, and tweets with images that do not contain tourist spot names. The experiments provided the following three results: (1) Tweets without tourist spot names tweeted before and after tweets containing tourist spot names contain tourist information. (2) Replies to tweets containing tourist spot names contain tourist information. (3) Tweets with images that do not contain tourist spot names contain information regarding the food and entertainment available at tourist spots.


Tourist information Information extraction Information analysis Twitter 


  1. 1.
    Japan Tourism Agency: Research study on economic impacts of tourism in Japan (2014).
  2. 2.
    Kazuya, H., Yusuke, K.: Research on the regional promotion through anime-tourism. In: 19th Conference of Japan Association for Evolutionary Economics, pp. 1–56 (2015)Google Scholar
  3. 3.
    Japan Tourism Agency: Change of visitor arrivals and Japanese overseas travelers.
  4. 4.
    Sayuri, W., Takashi, Y.: Tourist Information Visualization System for Improvement Discovery Based on the Similarity among Tourist Spots, Multimedia, Distributed, Cooperative, and Mobile Symposium, pp. 1357–1362 (2016)Google Scholar
  5. 5.
    Kazutaka, S., Shunsuke, I., Hiroshi, M., Tsutomu, E.: Analyzing tourism information on Twitter for a local city. In: 1st ACIS International Symposium on Software and Network Engineering (SSNE 2011), pp. 61–66 (2011)Google Scholar
  6. 6.
    Ritter, A., Mausam, E.O., Clark, S.: Open domain event extraction from Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 1104–1112 (2012)Google Scholar
  7. 7.
    Kenta, O., Koki, U., Fumio, H.: Mapping geotagged tweets to tourist spots for recommender systems. In: 2014 IIAI 3rd International Conference on Advanced Applied Informatics (IIAI 2014), pp. 789–794 (2014)Google Scholar
  8. 8.
    Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, pp. 1–10 (2010)Google Scholar
  9. 9.
    Sayuri, W., Takashi, Y.: Proposal of tourist information extraction methods from tweets without position information by tweets with position information and tweets containing tourist spots names, IPSJ Kansai-Branch Convention 2016, G-15, pp. 1–3 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate School of System EngineeringWakayama UniversityWakayamaJapan

Personalised recommendations