Title of Paper

Foreign tourist’s perceptions towards transport information and services using Twitter data mining: The case of typhoons in Japan

Author Bios (50 Words for each Author)

Sunkyung Choi is a specially appointed lecturer (associate professor) at the School of Environment and Society, Tokyo Institute of Technology in Japan. She has earned Ph.D. in transport planning and engineering. Her research interest includes tourism crisis management, tourism statistics and big data analysis, airport operation in disasters, and information provision for foreigners in disasters.

Shinya Hanaoka is a Professor at the School of Environment and Society, Tokyo Institute of Technology. He has worked as a Researcher for the Institute for Transport Policy Studies in Tokyo (1999-2003), an Assistant Professor for the Asian Institute of Technology (2003-2007), and a Visiting Researcher for the Institute for Transport Studies, University of Leeds (2002). He has authored and co-authored numerous journal articles and has participated in private and government-funded transport research projects. His research interests include transport logistics, transport development studies, air transport, and transport infrastructure management.

Abstract (150 Words)

Past disasters highlighted the negative perceptions of foreigner tourists with information and services. In terms of the frequency of disasters, the vulnerability of transport information and services during typhoon disasters has a great impact on foreigners, especially foreign tourists. And social media has the potential to provide an additional stream of data that used for understanding the public’s perceptions, especially during disasters. Therefore, a methodological framework for interpreting foreigners’ perceptions towards transport information and services from Twitter during typhoon disasters is presented in this research. A case study in Typhoon Faxai and Hagibis is used to implement the presented framework. A web crawler has been developed to extract tweets data posted within Japan from September 7 to 15, October 7 to 21, 2019. Second, after data pre-processing, basic text mining methods including Word Cloud and Co-Occurrence Network are conducted. Third, an embedding BERT topic model is built to generate topics of public and official side. Finally, evaluate perceived service quality quantified by sentiment scores using Topic-based VADER sentiment analysis.

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Foreign tourist’s perceptions towards transport information and services using Twitter data mining: The case of typhoons in Japan

Past disasters highlighted the negative perceptions of foreigner tourists with information and services. In terms of the frequency of disasters, the vulnerability of transport information and services during typhoon disasters has a great impact on foreigners, especially foreign tourists. And social media has the potential to provide an additional stream of data that used for understanding the public’s perceptions, especially during disasters. Therefore, a methodological framework for interpreting foreigners’ perceptions towards transport information and services from Twitter during typhoon disasters is presented in this research. A case study in Typhoon Faxai and Hagibis is used to implement the presented framework. A web crawler has been developed to extract tweets data posted within Japan from September 7 to 15, October 7 to 21, 2019. Second, after data pre-processing, basic text mining methods including Word Cloud and Co-Occurrence Network are conducted. Third, an embedding BERT topic model is built to generate topics of public and official side. Finally, evaluate perceived service quality quantified by sentiment scores using Topic-based VADER sentiment analysis.