Browse > Article
http://dx.doi.org/10.14400/JDC.2021.19.12.011

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

Yang, Ji-Yeon (Dept. of Applied Mathematics, Kumoh National Institute of Technology)
Koo, Jeong-Ho (Dept. of Business Administration, Kumoh National Institute of Technology)
Publication Information
Journal of Digital Convergence / v.19, no.12, 2021 , pp. 11-20 More about this Journal
Abstract
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.
Keywords
University education; newspaper articles; topic modeling; latent Dirichlet allocation; text mining;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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   DOI
2 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   DOI
3 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   DOI
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   DOI
5 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   DOI
6 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   DOI
7 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   DOI
8 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
9 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   DOI
10 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   DOI
11 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   DOI
12 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
13 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   DOI
14 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   DOI
15 KOSIS KOrean Statistical Information Service Statistics Korea, https://kosis.kr/statHtml/statHt ml.do?orgId=101&tblId=DT_1YL21181
16 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   DOI
17 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   DOI
18 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   DOI
19 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   DOI
20 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   DOI
21 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   DOI
22 BIG KINDS, News Bigdata & Analysis. Korea Press Foundation. https://www.bigkinds.or.kr
23 KESS, Korean Educational Statistics Service, https://kess.kedi.re.kr
24 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   DOI
25 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   DOI