디지털융복합연구 (Journal of Digital Convergence)

한국디지털정책학회 (The Society of Digital Policy and Management)
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뉴스 빅데이터를 통해 검토한 대학교육의 토픽 분석

A Topic Analysis of College Education Using Big Data of News Articles

  • 양지연 (금오공과대학교 응용수학과) ;
  • 구정호 (금오공과대학교 경영학과)
  • Yang, Ji-Yeon (Dept. of Applied Mathematics, Kumoh National Institute of Technology) ;
  • Koo, Jeong-Ho (Dept. of Business Administration, Kumoh National Institute of Technology)
  • 투고 : 2021.09.08
  • 심사 : 2021.12.20
  • 발행 : 2021.12.28
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초록

본 연구는 신문기사 빅데이터를 통해 대학교육 관련 보도의 토픽을 추출하고, 토픽별 특징 및 신문사별 보도양상을 분석한다. 2016년-2021년 상반기 주요 중앙지와 지역지의 기사를 빅카인즈를 통해 추출하였고, 잠재디리슐레할당을 이용하여 총 9개의 토픽을 발견하였다. 토픽1과 토픽3은 교육에 대한 대학지원사업에 관련된 것이나 토픽3은 지역대학에 초점이 맞추어져 있다. 토픽2는 코로나19 이후 대학교육, 토픽4는 교수-학습법, 토픽5는 정부정책, 토픽6은 고교교육기여대학 지원사업, 토픽7은 대학교육 비전, 토픽8은 국제화, 토픽9는 입시 등을 논하고 있다. 조선일보, 경향신문, 한겨레는 코로나19 이후 강의, 정부정책 관련, 대학교육에 대한 기사와 논평을 많이 보도한 반면 동아일보, 중앙일보, 한라일보, 부산일보, 대전일보, 경인일보는 대학지원사업, 고교교육기여대학 지원사업 등 광고·홍보성 기사가 상대적으로 많았다. 2016년부터의 관련기사를 신문사별 뿐 아니라, COVID-19 발생 전후로도 분석하여 관련 보도의 토픽 차이를 살펴볼 수 있었다. 사회적으로 주요 관심 사항인 대학교육이 언론에 어떻게 보도되고 있는지 확인함으로써 미래의 대학교육 정책 방향과 미디어의 순기능과 역기능 등 언론의 역할에 대해 고찰할 필요가 있음을 시사한다.

This study extracts topics related to university education through newspaper articles and analyzes the characteristics of each topic and the reporting patterns of each newspaper. The 9 topics were discovered using LDA. Topic 1 and Topic 3 are related to university support projects for education, but Topic 3 is focused on local universities. Topic 2 is about university education after COVID-19, Topic 4 teaching-learning methods, Topic 5 government policies, Topic 6 the high school education contribution university support projects, Topic 7 the university education vision, Topic 8 internationalization, and Topic 9 the entrance exam. The Chosun Ilbo, Kyunghyang, and Hankyoreh reported a lot of articles associated to lectures after COVID-19, government policies, and comments on university education. Relevant articles since 2016 have been analyzed by newspaper type and before/after COVID-19 through which differences in the topics were studied and discussed. These findings would suggest a basic policy guideline for university education and imply that the positive and negative effects of the media need to be considered.

키워드

과제정보

This paper was supported by Kumoh National Institute of Technology Research Grant in 2020 (No.20200231001)

참고문헌

  1. S. Noh. (2021). A Analysis of Issues Related to Artificial Intelligence Based on Topic Modeling. Journal of Digital Convergence, 18(5), 75-87. DOI : doi.org/10.14400/JDC.2020.18.5.075
  2. J. Ki & S. Ahn. (2020) Application of Sentiment Analysis and Topic Modeling on Rural Solar PV Issues: Comparison of News Articles and Blog Posts. Journal of Digital Convergence, 18(9), 17-27. DOI : doi.org/10.14400/JDC.2020.18.9.017
  3. S. S. Lee, I. Yoo & J. Kim (2020). An analysis of public perception on Artificial Intelligence(AI) education using Big Data: Based on News articles and Twitter. Journal of Digital Convergence, 18(6), 9-16. DOI : doi.org/10.14400/JDC.2020.8.6.009
  4. S. M. Kim. (2020). Analysis of Press Articles in Korean Media on Online Education related to COVID-19. Journal of Digital Contents Society, 21(6), 1091-1100. DOI: https://doi.org/10.9728/dcs.2020.21.6.1091
  5. S. M. Heo & J. Y. Yang. (2021). A Convergence Study on the Topic and Sentiment of COVID19 Research in Korea Using Text Analysis. Journal of the Korea Convergence Society, 12(4), 31-42. DOI : dx.doi.org/10.15207/JKCS.2021.12.4.031
  6. S. Yoon, S. Jung & Y. A. Kim. (2021). Trend Analysis of Corona Virus(COVID-19) based on Social Media, Journal of Korea Academia- Industrial cooperation Society, 22(5), 317-324. DOI : 10.5762/KAIS.2021.22.5.317
  7. I. S. Park. (2021). Analysis of press articles in Korean media on education policy of the Ministry of Education related to COVID-19. Teaching Practicum Research, 3(1), 10-21. http://www.riss.kr/link?id=A107781888
  8. S. M. Kim. (2020). Analysis of Press Articles in Korean Media on Online Education related to COVID-19. Journal of Digital Contents Society, 21(6), 1091-1100. DOI: https://doi.org/10.9728/dcs.2020.21.6.1091
  9. J. Kim, H. S. Na & K. H. Park. (2021). Topic Modeling of Profit Adjustment Research Trend in Korean Accounting. Journal of Digital Convergence, 19(1), 125-139. DOI : doi.org/10.14400/JDC.2021.19.1.125
  10. S. M. Kim & Y. J. Kim. (2020). Research Trend Analysis on Living Lab Using Text Mining. Journal of Digital Convergence, 18(8), 37-48. DOI : doi.org/10.14400/JDC.2020.18.8.037
  11. S. K. Park, H. J. Lee & B. G. Lee (2021) Exploring Social Issues of On-demand Delivery Platform Participants. Journal of Digital Convergence, 19(7), 79-85. DOI : doi.org/10.14400/JDC.2021.19.7.079
  12. S. M. Lee & S. G. Hong. (2020). Policy agenda proposals from text mining analysis of patents and news articles. Journal of Digital Convergence, 18(3), 1-12. DOI : doi.org/10.14400/JDC.2020.18.3.001
  13. M. J. Kim (2020). Analyzing the Trend of Wearable Keywordsusing Text-mining Methodology. Journal of Digital Convergence , 18(9), 181-190. DOI : doi.org/10.14400/JDC.2020.18.9.190
  14. M. S. Shon, M. J. Im & K. H. Park (2021). A Study on Consumer perception changes of online education before and after COVID-19 using text mining. Journal of Digital Convergence, 19(1), 29-43. DOI : doi.org/10.14400/JDC.2021.19.1.029
  15. BIG KINDS, News Bigdata & Analysis. Korea Press Foundation. https://www.bigkinds.or.kr
  16. D. M. Blei, A. Y. Ng & M. I. Jordan. (2003). Latent dirichlet allocation, The Journal of Machine Learning Research, 3, 993-1022. https://dl.acm.org/doi/10.5555/944919.944937
  17. S. M. Heo & J. Y. Yang. (2020). Analysis of Research Topics and Trends on COVID-19 in Korea Using Latent Dirichlet Allocation. Journal of The Korea Society of Computer and Information, 25(12), 83-91. DOI : 10.9708/jksci.2020.25.12.083
  18. M. L. Jockers & R. Thalken. (2014). Text analysis with R for students of literature,. New York: Springer. DOI : 10.1007/978-3-319-03164-4
  19. J. Cao, T. Xia, J. Li, Y. Zhang, & S. Tang. (2009). A density-based method for adaptive lda model selection, Neurocomputing, 72(7), 1775-1781. DOI: 10.1016/j.neucom.2008.06.011
  20. R. Arun, V. Suresh, C. V. Madhavan, & M. N. Murthy. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Part I, 391-402. DOI : 10.1007/978-3-642-13657-3_43
  21. T. L. Griffiths & M. Steyvers. (2004). Finding scientific topics. Proceedings of the National academy of Sciences. 101, suppl 1, 5228-5235. DOI: 10.1073/pnas.0307752101
  22. R. Deveaud, E. SanJuan, & P. Bellot. (2014). Accurate and effective latent concept modeling for ad hoc information retrieval. Document numerique. 17(1), 61-84. DOI: 10.3166/DN.17.1.61-84
  23. KOSIS KOrean Statistical Information Service Statistics Korea, https://kosis.kr/statHtml/statHt ml.do?orgId=101&tblId=DT_1YL21181
  24. C. Sievert & K. Shirley. (2014). LDAvis: A method for visualizing and interpreting topics. Conference: Workshop on Interactive Language Learning, Visualization, and Interfaces at the Association for Computational Linguistics. 63-70. DOI:10.13140/2.1.1394.3043
  25. KESS, Korean Educational Statistics Service, https://kess.kedi.re.kr