DOI QR코드

DOI QR Code

텍스트마이닝을 활용한 보건의료산업학회지의 토픽 모델링 및 토픽트렌드 분석

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

  • 조경원 (고신대학교 의료경영학과) ;
  • 배성권 (고신대학교 의료경영학과) ;
  • 우영운 (동의대학교 응용소프트웨어공학과)
  • 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)
  • 투고 : 2017.11.20
  • 심사 : 2017.12.21
  • 발행 : 2017.12.30

초록

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.

키워드

참고문헌

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