Development of a Disaster Safety Sentiment Index via Social Media Mining
Journal of Public Policy and Administration
Volume 3, Issue 1, March 2019, Pages: 29-38
Received: Mar. 7, 2019;
Accepted: Apr. 28, 2019;
Published: May 23, 2019
Views 133 Downloads 25
Seon Hwa Choi, Earthquake Hazard Reduction Center, National Disaster Management Research Institute, Ulsan, Korea
Follow on us
People use social media platforms such as Twitter to record their personal thoughts and opinions. Social media platforms reflect people’s sentiments as they are, and an accurate understanding of sentiments on social media could be useful and significant for disaster management. In this research, sentiment type modeling and sentiment quantification are proposed to understand the sentiments presented on social media platforms. Sentiment types are primarily analyzed based on the three major sentiments of affirmation, caution, and observation. Then, for a detailed understanding of sentiment progress according to the progress of a disaster or accident and the government’s response, negative sentiments are categorized into anxiety, disappointment, depression, sadness, and displeasure to enhance the analysis, while positive sentiments are categorized into pleasure, happiness, and relief; Russell’s circumplex model is used to develop a model of eight primary sentiments to acquire an overall understanding of the public’s sentiments. Then, the sentiment index of each sentiment is quantified. Based on the results, the overall sentiment status of the public is monitored, and in the event of a disaster, the public’s sentiment fluctuation rate can be quantitatively observed. Moreover, the influence of disasters and accidents on public sentiments, or the sentiment indices of different accidents, can be compared to identify the accidents that affect public sentiment and public needs after a disaster, and the insights can be used for policy-making.
Big Data, Disaster Management, Emotion Analysis, Social Media
To cite this article
Seon Hwa Choi,
Development of a Disaster Safety Sentiment Index via Social Media Mining, Journal of Public Policy and Administration.
Vol. 3, No. 1,
2019, pp. 29-38.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Jeon Y. S., Kim Y. S., & Jeong S. Y. (1995). Management. Gyeonggi-do: Bobmunsa.
Miller, J. (2012). Psychosocial Capacity Building in Response to Disasters, New York: Columbia University Press.
National Disaster Management Research Institute. (2015). Development of social media utilization guideline for disaster management and enlargement of analysis resources. Final report. Seoul: NDMI.
Choi, S. H., Lee, J. G., & Yeo W. G. (2014). Disaster management competence reinforcement for self-governing bodies using big data. Local Administration Monthly Magazine, 730, 16–19.
Liu, B. (2008). Opinion mining & summarization-sentiment analysis. Retrieved from: http://wwwconference.org/www2008/program/program-tutorials-TA6.html.
Sentiment analysis (2019). Retrieved from: http://en.wikipedia.org/wiki/Sentiment_analysis.
Back, B. H., Ha, I. K., & Ahn, B. C. (2014). An extraction method sentiment information from unstructured big data on SNS. Journal of Korea Multimedia Society, 17 (6), 671–680.
Ryu, P. M., Kim, H. J., Kim, H. K., and Park, S. K. (2012). Social media issue detection & monitoring based on deep language analysis techniques. Communications of Korean Institute of Information Scientists and Engineers, 30 (6), 47–58.
Shin, S. M., & Lee, T. S. (2015). A study on sentiment classification for SNS data analysis. Proceedings of Korean Institute of Information Scientists and Engineers Dec 17-19 2015, Pyeongchang: KIISE Press.
Cha, Y. S., Hwang, M. C., & Kim, S. L. (2011). An empirical study on modeling social emotion evoked during social network service. Proceedings of Korean Society of Emotion and Sensibility. Nov 11, 2011. Gyeongju: KSES Press.
National Disaster Management Research Institute. (2014). Development of social big data semantic monitoring technology. Final report. Seoul: NDMI.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39 (6), 1161–1178.
The Ministry of Public Safety and Security. The report on the perceived level of safety. (2016). Retrieved from: http://www.mois.go.kr/mpss/board/file/bbs_0000000000000041/8598/FILE_000000000005597/20160920173330107.pdf; jsessionid=X2l7nh3kzHu4R5SAcxFn3fuE.node11.