Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.10.537

Topic Modeling of Newspaper Articles on Government 'Senior job program' via Latent Dirichlet Allocation.  

Lee, So-Chung (Namseoul University, Department of Elderly Welfare)
Publication Information
Journal of Digital Convergence / v.18, no.10, 2020 , pp. 537-546 More about this Journal
Abstract
This study aims to find the structure of social disussion on government 'Senior job program' by analyzing 1107 newspaper articles on 'senior job program' from 11 major newspaper articles and 8 financial newspapers. Topic modeling via latent dirichlet allocation model was employed for analysis and as result, 5 latent topics were extracted as follows : general information, local government project propaganda, senior life related issues, employment creation effect and market relations. Until 2015, most of the articles focused on the first two topics, indicating not much discourse was formed concerning the characteristics of the program. However, after 2015, the third topic started to increase and after the launch of Moon Jae In government, there has been a drastic increase in the employment creation related topic indicating that current social discourse mirrored by the media is definitely focused on employment creation aspect of senior job program. Based on the result, this study suggests the necessity to increase the quality and also enhance employment aspects of Senior job program.
Keywords
Senior Job Program; Social discussion; newspaper articles; topic model; latent dirichlet allocation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Kim. (2015). The Politics of Representing the Pain of Others : Regarding the Sewol Ferry Disaster, Media and Society, 23(4), 67-119.
2 Ministry of Government Legislation, www.moleg.go.kr
3 G. Han & S. Yoon. (2007). Critical Review of Discourse on Aging in Korean Newspapers, Journal of the Korean Gerontological Society, 27(2), 299-322.
4 E. J. Kim. (2017). How Media Makes the Elderly into Welcoming Citizens in the Aged Society, Korean Journal of Journalism and Communications, 61(3), 157-188. http://doi.org/10.20879/kjjcs.2017.61.3.005
5 B. H. Lee & G.Y. Kim. (2019). The Themes and Trends of Discourse on the Elderly in Korea Identified by Analyzing Social Media Big Data, Social Welfare Policy, 46(3), 17-201.
6 H.J. Oh & K.A. Shin. (2019). How does Korean News Media Cover News Stories of Older Adults? A Content Analysis of Korean News Articles Published After 2010, Journal of Public Relations, 23(4), 40-68. DOI: 10.15814/jpr.2019.23.4.40   DOI
7 J. M. Lee. & Y. S. Park. (2018). A frame analysis of the conservative and progressive media on the Moon Jae In Government's welfare policy and budget, Journal of Budget and Policy, 7(2), 51-80.   DOI
8 S.D. Yoo & J.S. Lee. (2017). Media Interest and Policy Decision of Child Welfare Issue : An Empirical Study on the Downs' Issue-Attention Cycle, Journal of Government Administration, 13, 29-60.
9 Korea Labor Force Development Institute for the Aged, www.kordi.or.kr
10 I. K. Choi. (2012). Analysis on the types of the Reporting Behavior based on the Diffusion of Policy Issues : Focused on the Decision-making Process of the Basic Old Age Pension System, Journal of Regional Studies and Development, 21(1), 155-191.
11 Y. M. Kim. (2016). Discourse about Recipients of the National Basic Livelihood Security by the Press, Journal of Critical Social Policy, 53, 282-325.
12 Big Kinds, www.kinds.or.kr
13 Blei, D.M. & J. D. Lafferty. (2006). Dynamic topic models. In : Pohoreckhy, A., Bottou, L., & Littman, M.L.(eds.), Proceedings of the International Conference on Machine Learning. 113-120.
14 Silge, J. & Robinson, D. (2017). Text Mining with R : A Tidy Approach : O'Reilly.
15 DiMaggio, P., M. Nag & D. Blei. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture : Application to newspaper coverage of U.S. government arts funding, Poetics, 41, 570-606. http://dx.doi.org/10.1016/j.poetic.2013.08.004.   DOI
16 Blei, D.M., A. Ng, & M. Jordan. (2003). Latent Dirichlet Allocation, Journal of Machine Learning Research, 3, 993-1022.
17 Liu, L., L. Tang, W. Dong, S. Yao & W. Zhou. (2016). An overview of topic modeling and its current applications in bioinformatics, SpringerPlus, 5(1608), http://doi.org/10.1186/s40064-016-3252-8
18 McFarland, D.A., D. Ramage, J. Chuang, J. Heer, C.D. Manning & D. Jurafsky. (2013). Differentiating Language Usage Through Topic Models, Poetics, 41, 607-625.   DOI
19 Hall, D., D. Jurafsky. & C. Manning. (2008). Studying the history of ideas using topic models. In: Laputa, M., Ng, H.T.(Program Co-Chairs), Proceedings of the 2008 Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, 363-371.
20 B. R. Roh. & K. E. Yang. (2019). Text Mining Analysis of South Korea's Birth-rate Decline Issue in Newspaper Articles : Transition Patterns over 18 Years, Korean Journal of Social Welfare, 71(4), 154-176.   DOI
21 Y. M. Baek. (2019). Text-Mining Using R : Hanul Academy.
22 Sutherland, I., Y. Sim., S. K. Lee, J. Byun & K. Kiatkawsin. (2020). Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation, Susainability, 12(182). DOI: 10.3390/su12051821.