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LDA 기법을 이용한 미세먼지 이슈의 토픽모델링 분석

Topic Modeling on Fine Dust Issues Using LDA Analysis

  • 윤순욱 (녹색기술센터 정책연구부) ;
  • 김민철 (녹색기술센터 정책연구부)
  • 투고 : 2020.04.03
  • 심사 : 2020.05.20
  • 발행 : 2020.06.30

초록

본 연구에서는 최근 10년간의 미세먼지 관련 뉴스 데이터를 수집하여 LDA 분석을 통해 최적 토픽을 도출하였다. 최적 토픽으로 선별된 80개의 이슈를 미세먼지 정책의 시각에서 해석하였다. 연구결과, 기온과 같은 날씨와 관련된 정보와 미세먼지 농도가 관련되어서 이슈화되는 경향이 있었다. 다음으로 미세먼지 저감 대책의 일환으로 노후경유차 운행 제한 제도와 저감 장치 부착과 같은 이슈의 빈도수가 높았다. 국민에 대한 제도 변경 안내를 포함하여 시민과 운수업자와의 갈등도 주요한 토픽으로 나타났다. 미세먼지 문제의 해결을 위한 수소차 보급과 같은 대안도 주요 토픽으로 분석되었다. 또한 미세먼지 관련 공기청정기 등 제품 관련 주제, 취약계층을 미세먼지로부터 보호하는 정책과 관련된 주제, 연구개발을 통한 미세먼지 저감 관련 주제가 주요 화두로 제기되었다. 미세먼지 대책은 사회 이슈로 정부 정책과 밀접한 관련이 있다고 볼 수 있다. 또한 본 연구를 통해 토픽 상에서는 거시적인 정부정책 자체보다는 시민의 안전, 시혜적인 정책이나 이해관계자간의 갈등이 정부정책 변화와 연동하여 중요한 의미를 지니는 것으로 나타났다.

In this study, the last 10 years of news data on fine dust was collected and 80 topics are selected through LDA analysis. As a result, weather-related information made up the main words for the topic, and we can see that fine dust becomes a big issue below 10 degrees Celsius. The frequency of exposure to the media and the maximum concentration of fine dust are correlated with positive. Topics related to fine dust reduction measures and the government's comprehensive measures over the past decade, topics related to products such as air purifiers related to fine dust, topics related to policies protecting vulnerable people from fine dust, and topics on fine dust reduction through R&D were found to be major topics. Measures against fine dust as a social issue can be seen to be closely related to the government's policy.

키워드

참고문헌

  1. Green Technology Center Korea, 2019. A Study on the Development of Domestic Climatic Adaptation Industry in the 4th Industry Revolution Era (in Korean). Seoul
  2. Jeong JW, Lee JM, Choi SY. 2018. Analysis of news regarding the disabled labor using text mining techniques(in Korean). Reinterpretation of Disability, pp. 48-100
  3. Kang HJ, Kim C, Kang K. 2019. Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA). Processes Vol. 7, No. 6, www.doi:10.3390/pr7060379
  4. Keller TR, Hase V, Thaker J, Mahl D, Schafer M. 2020. News Media Coverage of Climate Change in India 1997-2016: Using Automated Content Analysis to Assess Themes and Topics. Environmental Communication Vol. 14, No. 2, www.doi: 10.1080/17524032.2020.1716033
  5. Kim JH, Cho JH. 2019. Investigation of Effects of Individuals Social Viewing of Fine Dust Information Obtained through Social Media on Behavioral Intentions of Disease Prevention : Application of Health Beliefs Model(in Korean). Korean Journal of Broadcasting and Telecommunication Studies Vol. 33, No. 4, pp.37-65
  6. Kim MC, Yoon SU, Kim HM. 2020. A Study on the Hydrogen Economic Law for the Realization of Hydrogen Society in Korea(in Korean). Soongsil Law Review 46, pp.1-30
  7. Kim MC. 2019. Legislation of climate change adaptation has become a global trend(in Korean), KACCC ADAPTATION
  8. Kim MC. 2019. Proactive Legislative Evaluation of Hydrogen Economy Legislation in Response to Climate Change, Korea Legislation Research Institute( in Korean), Legislative Evaluation Issue Paper 19-14-(1). 2-40
  9. Kim YW, Lee HS, Jang YJ, Lee HJ. 2015. How Does Media Construct Particulate Matter Risks? : A News Frame and Source Analysis on Particulate Matter Risks(in Korean). Korean Journal of Journalism & Communication Studies Vol. 59, No. 2, pp.121-154
  10. Korea Institute for Health & Social Affairs. 2018. Social big data trend analysis based on health and welfare issues in 2018(in Korean). Sejong.
  11. Lee EB, John JN, Baek JS. 2017. A Study of Multicultural Space in Seoul : Analysing the Coverage of Foreign Communities with News Big Data Analytics BigKinds for 27 Years(in Korean). Journal of Media Economics & Culture Vol. 15, No. 2, pp.7-43
  12. Moon MR, Kim MC, Kim JW. 2019 A Study on the Fine Dust-related Bills in the National Assembly - Based on the Revision of the Special Act on Fine Dust(in Korean). Hannam Journal of Law&Technology Vol. 25, No. 4, pp.87-115 https://doi.org/10.32430/ilst.2019.25.4.87
  13. Moon SH, Chung SH, Chi SH. 2018. Topic Modeling of News Article about International Construction Market Using Latent Dirichlet Allocation (in Korean). Journal of the Korean Society of Civil Engineers Vol. 38, No. 4, pp.595-599 https://doi.org/10.12652/KSCE.2018.38.4.0595
  14. Park HJ. et al.. 2015. Prediction of correct answer rate for English scholastic ability test using text mining. Industrial Engineering & Management Systems in Procedings, pp. 2,277-2,288
  15. Seo Dae-ho. 2019. Catch! Text Mining with Python( in Korean), BJpublic
  16. Soo-Sang Lee. 2018. Network Analysis Methods Applications and Limitations( in Korean), Chung-Ram
  17. Tran BX, Nghiem S, Sahin O, Vu T, Ha GH, Vu GT, Pham HQ, Do HT, Latkin CA,Tam W, Ho C, Ho R. 2019. Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study. J Med Internet Res www.doi: 10.2196/15511
  18. https://biz.chosun.com/site/data/html_dir/2018/04/09/2018040900053.html
  19. http://biz.newdaily.co.kr/site/data/html/2016/06/03/2016060310058.html
  20. https://www.yna.co.kr/view/AKR20170525088700061
  21. https://www.yna.co.kr/view/AKR20160811189000003
  22. https://www.sedaily.com/NewsVIew/1L1EHL6AHJ