Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Public Perception and Policy Implications of Foreign Workers through Social Big Data analysis

소셜 빅데이터분석을 통한 외국인근로자에 관한 국민 인식 분석과 정책적 함의

  • Ha, Jae-Been (Business Analytics and Information Systems, Ajou University) ;
  • Lee, Do-Eun (Korea University Legal Research Institute)
  • 하재빈 (아주대학교 비즈니스애널리틱스전공) ;
  • 이도은 (고려대학교 법학연구원)
  • Received : 2021.08.11
  • Accepted : 2021.11.20
  • Published : 2021.11.28
Download PDF
⟨ Previous Next ⟩

Abstract

This paper aimed to look at the awareness of foreign workers in social platforms by using text mining, one of the big data techniques and draw suggestions for foreign workers. To achieve this purpose, data collection was conducted with search keyword 'Foreign Worker' from Jan. 1, to Dec. 31, 2020, and frequency analysis, TF-IDF analysis, and degree centrality analysis and 100 parent keywords were drawn for comparison. Furthermore, Ucinet6.0 and Netdraw were used to analyze semantic networks, and through CONCOR analysis, data were clustered into the following eight groups: foreigner policy issue, regional community issue, business owner's perspective issue, employment issue, working environment issue, legal issue, immigration issue, and human rights issue. Based on such analyzed results, it identified national awareness of foreign workers and main issues and provided the basic data on policy proposals for foreign workers and related researches.

본 연구에서는 빅데이터 기법 중에 하나인 텍스트마이닝을 활용하여 소셜플랫폼에서 외국인근로자에 대한 인식을 알아보고 시사점을 도출하고자 하였다. 연구를 위해서 2020년 1월 1일부터 12월 31일까지를 기준으로 '외국인근로자' 검색 키워드를 수집하여 빈도분석, TF-IDF 분석, 연결중심성 분석으로 상위 키워드 100개를 도출하고 비교분석을 수행하였다. 또한 Ucinet6.0과 Netdraw를 이용해 의미연결망을 분석하였으며, CONCOR 분석을 통해 외국인정책 이슈, 지역사회 이슈, 사업주 관점 이슈, 고용 이슈, 근로환경 이슈, 법적 이슈, 출입국 이슈, 인권 이슈로 8개 클로스터로 군집화하였다. 이러한 분석 결과를 바탕으로 외국인근로자 국민적 인식, 주요 이슈를 파악하였으며, 향후 외국인근로자에 대한 정책 및 관련 연구에 필요한 기초자료를 제공하고자 한다.

Keywords

References

  1. K. T. Oh. (2007). Korean Workers' Perception to Foreign Workers. Labor Review, 25, 59-68.
  2. J. H. Heo, S. Y. Yu & C. J. Lee. (2020). A Qualitative Study of Intergenerational Differences in Perception on Labor Immigration between Korean 20s and 50s Residing in the Metropolitan Area. Social Science Research Review, 36(3), 1-29. DOI : 10.18859/ssrr.2020.8.36.3.1
  3. Y. J. Im. (2012). Comparative Analysis of Media on Migrant Workers in Korea : With an emphasis analyhsing Chosun Ilbo, Hankyoreh, Kyoungin and Busan Ilbo. Journal of Communication Science, 12(4), 419-456.
  4. T. H. Kim & S. W. Kim. (2019). Social Tendency and Network Analysis of High School Credit System. Journal of Educational Innovation Research, 29(2), 225-242. DOI : 10.21024/pnuedi.29.2.201906.225
  5. D. H. Seol. (2005). Migrant Workers and Human Rights. Journal of Democracy and Human Rights, 5(2), 39-77.
  6. S. T. Roh & N. C. Kim. (2007). The Human Rights of Aliens in Korea : Focusing on the Impact of International Human Rights Law on Their Situation. LAW REVIEW, 48(1), 255-288.
  7. Ministry of Justice Korea Immigration Service. (2018). Korea Immigration Service Statistics 2017. http://www.immigration.go.kr/bbs/immigration/228/4 40604/artclView.do
  8. Ministry of Justice Korea Immigration Service. (2019). Korea Immigration Service Statistics 2018. http://www.immigration.go.kr/bbs/immigration/228/5 03695/artclView.do
  9. Ministry of Justice Korea Immigration Service. (2020). Korea Immigration Service Statistics 2019. http://www.moj.go.kr/bbs/immigration/228/527712/artclView.do
  10. Sulova, S., Todoranova, L., Penchev, B., & Nacheva, R. (2017). Using text mining to classify research papers. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 17(21), 647-654. DOI : 10.5593/sgem2017/21/S07.083
  11. Feldman, R., & Dagan, I. (1995). Knowledge Discovery in Textual Databases (KDT). In KDD 95, 112-117.
  12. Y. M. Jung. (2012). Research in Information Retrieval, Seoul : Yonsei University Press
  13. J. H. Lee & S. H. Lee. (2018). A Case Study on Characteristics of Gender and Major in Career Preparation of University Students from Low-income Families: Application of Text Frequency Analysis and Association Rules. Journal of Digital Convergence, 16(12), 61-69. DOI : 10.14400/JDC.2018.16.12.061
  14. N. G. Kim, D. H. Lee & H. C. Choi. (2017). Investigations on Techniques and Applications of Text Analytics. The Journal of Korean Institute of Communications and Information Sciences, 42(2), 471-492. DOI : 10.7840/kics.2017.42.2.471
  15. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523. DOI : 10.1016/0306-4573(88)90021-0
  16. H. J. Kim, N. O. Jo & K. S. Shin. (2015). Text Mining-Based Emerging Trend Analysis for the Aviation Industry. Journal of Intelligence and Information Systems, 21(1), 65-82. DOI : 10.13088/jiis.2015.21.1.65
  17. Wang, W., & Rada, R. (1998). Structured hypertext with domain semantic. ACM Transactions on Information Systems (TOIS), 16(4), 372-412. DOI : 10.1145/291128.291132
  18. H. W. Park & Loet Leydesdorff. (2004). Understanding the KrKwic: A computer program for the analysis of Korean text. Journal of the Korean Data Analysis Society, 6(5), 1377-1387.
  19. S. S. Lee. (2014). A Content Analysis of Journal Articles Using the Language Network Analysis Methods. Journal of the Korean Society for information Management, 31(4),49-68. DOI : 10.3743/KOSIM.2014.31.4.049
  20. Y. J. Choi & S. H. Kweon. (2014). A Semantic Network Analysis of the Newspaper Articles on Big data. Journal of Cybercommunication Academic Society, 31(1), 241-286.
  21. Y. H. Kim & Y. J. Kim. (2016). Social Network Analysis, Seoul : ParkYoungSa
  22. K. Y. Kwahk. (2014). Social Network Analysis. Seoul : Cheong-Ram
  23. K. J. Han. (2003). The Meaning and Research Agenda in Network Analysis as Social Science Methodology -based on semantic network analysis-. Research in Social Studies Education, 10(2), 219-235.
  24. J. W. Kang & Y. Namkung. (2020). Understanding Consumers" Perceptions of the Fresh-Food Delivery Platform Service Based on Big Data: Using Text Mining and Semantic Network Analysis. Korean Journal of Hospitality & Tourism 30(2), 37-52. DOI : 10.24992/KJHT.2021.2.30.02.37
  25. J. S. Lee & C. Y. Kim. (2020). Popularization of Marathon through Social Network Big Data Analysis : Focusing on JTBC Marathon. Journal of the Korea Entertainment Industry Association 14(3), 27-30. DOI : 10.21184/jkeia.2020.4.14.3.27
  26. J. H. Lee, J. M. Lee, J. H. Kim & H. G. Kim. (2017). A Study on the Perception Change in Marine Sports by Social Media Big Data Analysis. Korean Journal of Sport Management, 22(1), 31-46. DOI : 10.31308/KSSM.22.1.3
  27. Y. J. Kim & D. Y. Kim. (2018). Methodology of Local Government Policy Issues Through Big Data Analysis. Journal of the Korea Contents Association, 18(10), 229-235. DOI : 10.5392/JKCA.2018.18.10.229