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http://dx.doi.org/10.14191/Atmos.2020.30.3.237

Text Mining and Network Analysis of News Articles for Deriving Socio-Economic Damage Types of Heat Wave Events in Korea: 2012~2016 Cases  

Jung, Jae In (Impact forecast Promotion Team, Korea Meteorological Administration)
Lee, Kyoungjun (Impact forecast Promotion Team, Korea Meteorological Administration)
Kim, Seungbum (High Impact Weather Research Department, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.30, no.3, 2020 , pp. 237-248 More about this Journal
Abstract
In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.
Keywords
Text-mining; natural language processing; network analysis; heat wave; weather impacts;
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1 Bastian, M., S. Heymann, and M. Jacomy, 2009: Gephi: An open source software for exploring and manipulating networks. Proc., In Third international AAAI conference on weblogs and social media, San Jose, California, the Association for the Advancement of Artificial Intelligence, 2 pp.
2 Blondel, V. D., J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, 2008: Fast unfolding of communities in large networks. J. Stat. Mech.-Theory E., 10, P10008.
3 Chae, Y., K. Cho, S. Lee, H. Jeon, Y. Chung, J. Lee, H. Park, and D.-K. Yoon, 2016: An analysis of the multiple impacts and policy networks of an extreme flood event in a metropolitan area. KEI 2016-14, 162 pp (in Korean).
4 GTC, 2015: Big data analysis of disaster caused by climate change. Green Technology Center, 129 pp (in Korean).
5 Gupta, V. and G. S. Lehal, 2009: A survey of text mining techniques and applications. J. Emerging Technologies in Web Intelligence, 1, 60-76.
6 Hagberg, A. A., D. A. Schult, and P. J. Swart, 2008: Exploring network structure, dynamics, and function using NetworkX. Proc. The 7th Python in Science Conference (SciPy2008), 11-15.
7 Jacomy, M., T. Venturini, S. Heymann, and M. Bastian, 2014: ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE, 9, e98679, doi:10.1371/journal.pone.0098679.   DOI
8 Kim, D.-W., J.-H. Chung, J.-S. Lee, and J.-S. Lee, 2014: Characteristics of heat wave mortality in Korea. Atmosphere, 24, 225-234, doi:10.14191/Atmos.2014.24.2.225 (in Korean with English abstract).   DOI
9 KCDC, 2016: Annual Report on the Notified Patients with Heat-related illness in Korea. Korea Centers for Disease Control and Prevention, 51 pp [Available online at http://www.nih.go.kr/contents.es?mid=a20304010700] (in Korean).
10 MPSS, 2016: Statistical Yearbook of Natural Disaster 2015. Ministry of Public Safety and Security, 184 pp (in Korean).
11 Mittermayer, M.-A., 2004: Forecasting Intraday Stock Price Trends with Text Mining Techniques. Proc.The 37th Hawaii International Conference on Social Systems, Hawaii, IEEE, 10 pp, doi:10.1109/HICSS.2004.1265201.
12 Nassirtoussi, A. K., S. Aghabozorgi, T. Y. Wah, and D. C. L. Ngo, 2014: Text mining for market prediction: a systematic review. Expert Syst. Appl., 41, 7653-7670, doi:10.1016/j.eswa.2014.06.009.   DOI
13 Newman, M. E. J., 2006: Modularity and community structure in networks. Proc. The National Academy of Sciences, 103, 8577-8582, doi:10.1073/pnas.0601602103.   DOI
14 NIMS, 2011: Report on climate change scenario 2011. National Institute of Meteorological Sciences, 117 pp (in Korean).
15 MOIS, 2017: Announcement of government-wide heat wave measures 2017. Ministry of the Interior and Safety, 15 pp (in Korean).
16 Park, E. L., and S. Cho, 2014: KoNLPy: Korean natural language processing in Python. Proc. The 26th Annual Conference on Human & Cognitive Language Technology, SIGHCLT, 133-136 (in Korean).
17 Park, S. B., 2012: Algal blooms hit South Korean rivers. Nature, doi:10.1038/nature 2012.11221.
18 Rickman, T. A., and R. M. Cosenza, 2007: The changing digital dynamics of multichannel marketing: The feasibility of the weblog: text mining approach for fast fashion trending. J. Fashion Marketing and Management, 11, 604-621, doi:10.1108/13612020710824634.   DOI
19 Sun, H., C. Lim, and Y. S. Lee, 2017: Analysis of the yearbook from the Korea Meteorological Administration using a text-mining algorithm. The Korean Journal of Applied Statistics, 30, 603-613, doi:10.5351/KJAS.2017.30.4.603 (in Korean with English abstract).   DOI
20 Won, J.-Y., and D.-G. Kim, 2014: Deduction of social risk issues using text mining. J. Safety and Crisis Management, 10, 33-52 (in Korean with English abstract).
21 WMO, 2015: WMO Guidelines on Multi-Hazard Impactbased Forecast and Warning Services. World Meteorological Organization, 23 pp.