The Korean Journal of Health Service Management (보건의료산업학회지)

Korea Society of Health Service Management (보건의료산업학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis on Topic Trends and Topic Modeling of KSHSM Journal Papers using Text Mining

텍스트마이닝을 활용한 보건의료산업학회지의 토픽 모델링 및 토픽트렌드 분석

  • Cho, Kyoung-Won (Department of Health Care Administration, Kosin University) ;
  • Bae, Sung-Kwon (Department of Health Care Administration, Kosin University) ;
  • Woo, Young-Woon (Department of Applied Software Engineering, Dong-Eui University)
  • 조경원 (고신대학교 의료경영학과) ;
  • 배성권 (고신대학교 의료경영학과) ;
  • 우영운 (동의대학교 응용소프트웨어공학과)
  • Received : 2017.11.20
  • Accepted : 2017.12.21
  • Published : 2017.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Objectives : The purpose of this study was to analyze representative topics and topic trends of papers in Korean Society and Health Service Management(KSHSM) Journal. Methods : We collected English abstracts and key words of 516 papers in KSHSM Journal from 2007 to 2017. We utilized Python web scraping programs for collecting the papers from Korea Citation Index web site, and RStudio software for topic analysis based on latent Dirichlet allocation algorithm. Results : 9 topics were decided as the best number of topics by perplexity analysis and the resultant 9 topics for all the papers were extracted using Gibbs sampling method. We could refine 9 topics to 5 topics by deep consideration of meanings of each topics and analysis of intertopic distance map. In topic trends analysis from 2007 to 2017, we could verify 'Health Management' and 'Hospital Service' were two representative topics, and 'Hospital Service' was prevalent topic by 2011, but the ratio of the two topics became to be similar from 2012. Conclusions : We discovered 5 topics were the best number of topics and the topic trends reflected the main issues of KSHSM Journal, such as name revision of the society in 2012.

Keywords

References

  1. National Research Foundation of Korea(2017), https://www.nrf.re.kr/
  2. D. Paranyushkin(2011), Visualization of text's polysingularity using network analysis, Prototype Letters, Vol.2(3);256-278.
  3. Y. Cho, P. Fu, C. Wu(2017), Popular Research Topics in Marketing Journals, 1995-2014, Journal of Interactive Marketing, Vol.40;52-72. https://doi.org/10.1016/j.intmar.2017.06.003
  4. I. Flaounas, S. Sudhahar, T. Lansdall-Welfare, E. Hensiger, N. Cristianini(2012), Big Data Analysis of News and Social Media Content, www.see-a-pattern.org
  5. D. Scanfeld, V. Scanfeld, E.L. Larson(2010), Dissemination of health information through social networks: Twitter and antibiotics, American Journal of Infection Control, Vol.38(3);182-188. https://doi.org/10.1016/j.ajic.2009.11.004
  6. M. Michelson, S.A. Macskassy(2010), Discovering users' topics of interest on twitter: a first look, ACM, pp.73-80.
  7. R. Chen, W. Xu(2017), The determinants of online customer ratings: a combined domain ontology and topic text analytics approach, Electronic Commerce Research, Vol.17(1);31-50. https://doi.org/10.1007/s10660-016-9243-6
  8. Z. Qiao, X. Zhang, M. Zhou, G.A. Wang, W. Fan(2017), A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews, Proceedings of the 50th Hawaii International Conference on System Sciences, pp.1821-1830.
  9. D.M. Blei, A.Y. Ng, M.I. Jordan(2003), Latent dirichlet allocation, Journal of machine Learning research, Vol.3(Jan);993-1022.
  10. J. Sleeman, M. Halem, T. Finin, M. Cane(2016), Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports, Synthesis, Vol.99(81);89.
  11. A.P. Shiryaev, A.V. Dorofeev, A.R. Fedorov, L.G. Gagarina, V.V. Zaycev(2017), LDA models for finding trends in technical knowledge domain, IEEE, pp.551-554.
  12. H.S. Choi, W.S. Lee, S.Y. Sohn(2017), Analyzing research trends in personal information privacy using topic modeling, Computers & Security, Vol.67;244-253. https://doi.org/10.1016/j.cose.2017.03.007
  13. A. Amado, P. Cortez, P. Rita, S. Moro(2017), Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis, European Research on Management and Business Economics, Vol.24;1-7.
  14. L. Sun, Y. Yin(2017), Discovering themes and trends in transportation research using topic modeling, Transportation Research Part C: Emerging Technologies, Vol.77;49-66. https://doi.org/10.1016/j.trc.2017.01.013
  15. J.Y. Moon(2009), Understanding the Intellectual Structure of Secretarial Studies with Text Mining, Journal of secretarial studies, Vol.18(1);83-98.
  16. S.G. Cho, S.B. Kim, Finding Meaningful Pattern of Key Words in IIE Transactions Using Text Mining, Journal of the Korean Institute of Industrial Engineers, Vol.38(1);67-73. https://doi.org/10.7232/JKIIE.2012.38.1.067
  17. J. H Park, M. Song(2013), A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling, Journal of the Korean society for information management, Vol.30(1);7-32. https://doi.org/10.3743/KOSIM.2013.30.1.007
  18. R. Mitchell(2015), Web scraping with Python: collecting data from the modern web, O'Reilly Media, Inc, pp.1-256
  19. J. Silge, D. Robinson(2017), Text mining with R: A tidy approach, O'Reilly Media, Inc, pp.1-194
  20. J. Kim, S. Baek(2016), Analysis of Issues on the College and University Structural Reform Evaluation Using Text Big Data Analytics, Asian journal of education, Vol.17(3);409-436. https://doi.org/10.15753/aje.2016.09.17.3.409
  21. B. Grun, K. Hornik(2011), topicmodels: An R package for fitting topic models, https://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf
  22. J. Ahn, J. Suh(2017), Economic Effects of South Korea's Smart Healthcare Industry, Korea Journal of health service management, Vol.11(2);55-64.

Cited by

  1. An analysis of indoor environment research trends in Korea using topic modeling : Case study on abstracts from the journal of the Korean society for indoor environment vol.17, pp.4, 2018, https://doi.org/10.15250/joie.2018.17.4.322
  2. A Study on the Change of Issues with Adolescent Problem by Using Text Mining - The Internet News Articles for the Years 2008 to 2018 - vol.28, pp.4, 2017, https://doi.org/10.21024/pnuedi.28.4.201812.461
  3. 2007-2018년 보건의료산업학회지에 게재된 간호연구 동향 분석 vol.13, pp.4, 2017, https://doi.org/10.12811/kshsm.2019.13.4.033
  4. 텍스트마이닝 기법을 활용한 미국산업응용수학 학회지의 연구 현황 및 동향 분석 vol.20, pp.7, 2017, https://doi.org/10.5392/jkca.2020.20.07.212
  5. 토픽모델링을 활용한 SIAM Journal on Applied Mathematics의 연구 동향 분석 vol.21, pp.7, 2017, https://doi.org/10.5762/kais.2020.21.7.607
  6. 잠재디리클레할당을 이용한 한국학술지인용색인의 풍력에너지 문헌검토 vol.16, pp.4, 2017, https://doi.org/10.7849/ksnre.2020.0020