This study aims to investigate the attitude within Asia-Pacific countries towards disaster risk reduction (DRR) through text mining of the official statements of the 2018 Asian Ministerial Conference on Disaster Risk Reduction. The official statements can be considered as a proxy of the participating countries’ stances on DRR. As methodology, four different kinds of text mining techniques were adopted; namely, word frequency list, hierarchical cluster analysis, co-occurrence network, and correspondence analysis for the sake of quantitative content analysis. Consequently, the word frequency list showed that words such as “development (develop)” and “climate change” seemed to be distinctive of the conference focusing on DRR issues. The result of hierarchical cluster analysis seemed to imply that the participating countries, namely their governments, had appeared to be keen to implement the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) along with their national DRR policies and to connect DRR with their development, while climate change had not been directly linked to the SFDRR and was stated as another global issue closely related to DRR. Considering that the SFDRR is closely related to the SDGs and the Paris Agreement, the observation of these contrasting results of the text mining analysis is a noteworthy finding. The result is also consistent with that of the co-occurrence network. The result of the correspondence analysis implied that the statement announced by Japan had appeared to have a characteristic feature in comparison to other statements. One possible reason for this is that there was no explicit reference to climate change, while the countries faced with disasters caused by climate change, such as those in the Pacific Islands, tended to focus on it.

1. [1] UNISDR, “Sendai Framework for Disaster Risk Reduction 2015-2030,” Geneva, Switzerland: United Nations Office for Disaster Risk Reduction (UNISDR), 2015.
2. [2] Government of Mongolia and UNISDR, “Asian Ministerial Conf. on Disaster Risk Reduction, 03 - 06 July 2018, Ulaanbaatar, Mongolia, Concept Note,” Geneva, Switzerland: United Nations Office for Disaster Risk Reduction (UNISDR), 2018.
3. [3] T. Ito, M. Miyamoto, and Y. Ono, “Strengthening Governance on Disaster Risk Reduction Through Improved Disaster Damage Statistics,” J. Disaster Res., Vol.11, No.3, pp. 470-475, 2016.
4. [4] D. Sasaki and Y. Ono, “Overview of the Special Issue on the Development of Disaster Statistics,” J. Disaster Res., Vol.13, No.6, pp. 1002-1006, 2018.
5. [5] D. Sasaki, K. Moriyama, and Y. Ono, “Hidden Common Factors in Disaster Loss Statistics: A Case Study Analyzing the Data of Nepal,” J. Disaster Res., Vol.13, No.6, pp. 1032-1038, 2018.
6. [6] S. Egawa, Y. Jibiki, D. Sasaki, Y. Ono, Y. Nakamura, T. Suda, and H. Sasaki, “The Correlation Between Life Expectancy and Disaster Risk,” J. Disaster Res., Vol.13, No.6, pp. 1049-1061, 2018.
7. [7] D. Sasaki, “A Comparative Analysis of Government Attitudes Towards Disaster Risk Reduction: A Text Mining Analysis of the Ministerial and Official Statements at the 2018 Asian Ministerial Conf. on Disaster Risk Reduction,” Proc. of 29th Annual Conf. of The Japan Society For Int. Development, 2018.
8. [8] O. Murao and H. Sakaba, “Quantitative Text Analysis of Sendai Framework for Disaster Risk Reduction 2015–2030,” J. Disaster Res., Vol.11, No.3, pp. 459-469, 2016.
9. [9] Y. Kobayashi and T. Munakata, “Extracting Disaster Characteristics Using Text Mining,” 2018 IEEE Int. Conf. on Big Data and Smart Computing, pp. 619-622, 2018.
10. [10] A. Hasegawa, M. Takahashi, M. Nemoto, T. Ohba, C. Yamada, S. Matsui, M. Fujino, and K. Satoh, “Lexical analysis suggests differences between subgroups in anxieties over radiation exposure in Fukushima,” J. of Radiation Research, Vol.59, No.S2, pp. ii83-ii90, 2018.
11. [11] L. Tang, B. Bie, and D. Zhi, “Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease,” American J. of Infection Control, Vol.46, No.12, pp. 1375-1380, 2018.
12. [12] Q. Huang, G. Cervone, and G. Zhang, “A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data,” Computers, Environment and Urban Systems, Vol.66, pp. 23-37, 2017.
13. [13] N. Yun and S. Lee, “Analysis of effectiveness of tsunami evacuation principles in the 2011 Great East Japan tsunami by using text mining,” Multimedia Tools And Applications, Vol.75, No.20, pp. 12955-12966, 2016.
14. [14] Q. Huang and Y. Xiao, “Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery,” ISPRS Int. J. of Geo-Information, Vol.4, No.3, pp. 1549-1568, 2015.
15. [15] H. Khalid, M. Helander, and N. Hood, “Visualizing disaster attitudes resulting from terrorist activities,” Applied Ergonomics, Vol.44, No.5, pp. 671-679, 2013.
16. [16] N. Öztürk and S. Ayvaz, “Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis,” Telematics and Informatics, Vol.35, No.1, pp. 136-147, 2018.
17. [17] B. Cannon, M. Nakayama, D. Sasaki, and A. Rossiter, “Shifting Policies in Conflict Arenas: A Cosine Similarity and Text Mining Analysis of Turkey’s Syria Policy, 2012-2016,” J. of Strategic Security, Vol.11, No.4, pp. 1-19, 2018.
18. [18] J. Luo, R. Wang, D. Suny, Y. Wang, and G. Li, “Comparison Among Four Prominent Text Processing Tools,” 2018 15th Int. Symp. on Pervasive Systems, Algorithms and Networks, pp. 325-330, 2018.
19. [19] K. Higuchi, “A Two-Step Approach to Quantitative Content Analysis : KH Coder Tutorial using Anne of Green Gables (Part I),” Ritsumeikan Social Sciences Review, Vol.52, No.3, pp. 77-91, 2016.
20. [20] K. Higuchi, “A Two-Step Approach to Quantitative Content Analysis : KH Coder Tutorial Using Anne of Green Gables (Part II),” Ritsumeikan Social Sciences Review, Vol.53, No.1, pp. 137-147, 2017.
21. [21] K. Higuchi, “KH Coder 3 Reference Manual,” 2016, https://khcoder.net/en/manual_en_v3.pdf [accessed May 4, 2019]