Journal of Korean Society of Archives and Records Management (한국기록관리학회지)

Korean Society of Archives and Records Management (한국기록관리학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Archival Information Services of Economic Policy Using Text Mining Methods: Focusing on Economic Policy Directions

텍스트 마이닝을 활용한 경제정책기록서비스 연구: 경제정책방향을 중심으로

  • 연지현 (충남대학교 일반대학원 기록관리학과) ;
  • 김성원 (충남대학교 사회과학대학 문헌정보학과)
  • Received : 2022.04.20
  • Accepted : 2022.05.15
  • Published : 2022.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

The archival content listed arbitrarily makes it difficult for users to efficiently access the records of major economic policies, especially given that they use it without understanding the required period and context. Using the text mining techniques in the 30-year economic policy direction from 1991 to 2021, this paper derives economic-related keywords and changes that the government mainly dealt with. It collects and preprocesses major economic policies' background, main content, and body text and conducts text frequency, term frequency-inverse document frequency (TF-IDF), network, and time series analyses. Based on these analyses, the following words are recorded in order of frequency: "job(일자리)," "competitive(경쟁력)," and "restructuring(구조조정)." In addition, the relative ratio of "job (일자리)," "real estate(부동산)," and "corporation(기업)," by year was analyzed in terms of chronological order while presenting major keywords mentioned by each government. Based on the results, this study presents implications for developing and broadening the area of archival information services related to economic policies.

자의적으로 구성한 기록 콘텐츠만으로는 이용자가 필요한 기간과 맥락에 대한 이해 없이 이용하게 됨으로써 주요한 경제정책기록에 효율적으로 접근하기에 어려움을 겪는다. 이러한 현재의 기록 서비스를 개선하기 위한 방안을 모색하고자 한다. 본 연구에서 1991년부터 2021년까지 30년간의 경제정책방향을 대상으로 경제정책기록에 텍스트 마이닝 기법을 활용하여 정부별 주요하게 다뤄진 경제 키워드와 변화과정을 도출하였다. 대책 배경, 주요 내용, 본문 텍스트를 수집하여 전처리를 진행한 후 텍스트 빈도분석, TF-IDF, 네트워크분석, 시계열 분석을 진행하였다. 분석 결과 '일자리', '경쟁력', '구조조정' 순으로 가장 높은 빈도수를 기록하였다. 정부별로 주요 키워드를 한눈에 볼 수 있었으며 '일자리', '부동산', '기업'의 연도별 상대비율을 시계열 순으로 분석하였다. 본 연구 결과를 바탕으로 향후 경제정책기록서비스의 발전과 저변확대를 위한 시사점을 제언하였다.

Keywords

References

  1. Cho, Jane (2011). A study for research area of library and information science by network text analysis. Journal of the Korean Society for information Management, 28(4), 65-83 https://doi.org/10.3743/KOSIM.2011.28.4.065
  2. Choi, kwang (2018). The Miracle of the Korean Economy 70 Years, Paju: Book&People.
  3. Hyun, Jung-taek (2006). Understanding of economic policy and policy implications on recent economic conditions. KDI Policy Forum, 172.
  4. Kang, Beomil, Song, Min, & Jho, Whasun (2013). A study on opinion mining of newspaper texts based on topic modeling. Journal of the Korean Society for Library and Information Science, 47(4), 315-334. https://doi.org/10.4275/KSLIS.2013.47.4.315
  5. Kim, Jun hyeon (2015). An essay for understanding the meaning of the network text analysis results in study of the public administration. Institute for Humanities and Social Sciences, 16(4), 247-280. https://doi.org/10.15818/ihss.2015.16.4.247
  6. Kim, Soo hyon, Lee, Young Joon, Shin, Jhinyoung, & Park, Ki Young (2020). Text mining for economic analysis. Journal of Korean Economic Analysis, 26(1), 1-70. http://dx.doi.org/10.22823/jkea.26.1.202004.1
  7. Kim, Yeon ji (2021). A Study on the Trend Analysis of Metadata in Journals of Library and Information Science: Using Network Analysis Techniques. Master's thesis, Sangmyung University.
  8. Korea Development Institute (2020). A Clear History of Korean Economic Development. Sejong: Korea Development Institute.
  9. Korea Development Institute (2020.9.21.) Economic policy directions. Economic policy timeline. Available: http://epts.kdi.re.kr/polcTmsesSrvc/them#this
  10. Korea Development Institute (2021). 50th Anniversary Vision Report. Sejong: Korea Development Institute.
  11. Lee, Jae-Yun, Moon, Ju-Young, & Kim, Hee-Jung (2007). Examining the intellectual structure of records management & archival science in korea with text mining. Journal of the Korean Society for Library and Information Science, 41(1), 345-372. https://doi.org/10.4275/KSLIS.2007.41.1.345
  12. National Institute of the Korean Language (2021.11.3.). Onlineganada. Available: https://www.korean.go.kr/front/main.do
  13. Park, Euntae (2004). A Dictionary of Economics. Seoul: Kyungyeonsa.
  14. Park, Jun Hyeong, Ryu, Pum-Mo, & Oh, Hyo-Jung (2018). Timeline-based topic trend analysis of archives management in korea. Journal of Korean Society of Archives and Records Management, 12(1), 29-47. https://doi.org/10.14404/JKSARM.2018.18.1.029
  15. Presidential Archives (2013). Policy record. Archive collection. Available: https://www.pa.go.kr/research/contents/policy/policymain.jsp
  16. Sin, Dongheon (2018). Implementation of Moving Job-Recommender System Based on TF-IDF: Focusing on reviews posted on the Jobplanet, Master's thesis, Soongsil University.
  17. Song, Hye-Ji, Park, Kyoung-Soo, Jung, Hye-Eun, & Song, Min (2013). Trend analysis of korean economy in the economic literature by text mining techniques. Proceedings of the Korean Society for Information Management Conference, 20, 47-50.
  18. Duff, W. & Yakel, E. (2017). Archival interaction, Currents of Archival Thinking(2nd),
  19. Schellenberg, T. S. (1956). Mordern archives: Principles and Techniques
  20. Wamboldt, B. D. (1992). Content analysis: Method, applications, and issues, 14, 313-321. https://doi.org/10.1080/07399339209516006
  21. Weber, R. P. (1990). Basic Content Analysis