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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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.
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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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