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

뉴스 기사 텍스트 마이닝과 네트워크 분석을 통한 폭염의 사회·경제적 영향 유형 도출: 2012~2016년 사례

  • 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)
  • 정재인 (기상청 영향예보추진팀) ;
  • 이경준 (기상청 영향예보추진팀) ;
  • 김승범 (국립기상과학원 재해기상연구부)
  • Received : 2020.05.18
  • Accepted : 2020.07.19
  • Published : 2020.09.30
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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.

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References

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