텍스트마이닝을 활용한 보건의료산업학회지의 토픽 모델링 및 토픽트렌드 분석
Analysis on Topic Trends and Topic Modeling of KSHSM Journal Papers using Text Mining
0
조회
0
다운로드
22
인용
0
참고문헌
키워드
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.
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.
참고문헌 (22)
- "National Research Foundation of Korea" (2017)[URL]
- D. Paranyushkin, "Visualization of text’s polysingularity using network analysis", Prototype Letters, 2(3), pp.256~278 (2011)
- Y. Cho, "Popular Research Topics in Marketing Journals, 1995-2014", Journal of Interactive Marketing, 40, pp.52~72 (2017)
- D. Scanfeld, "Dissemination of health information through social networks: Twitter and antibiotics", American Journal of Infection Control, 38(300), pp.182~188 (2010)
- Discovering users' topics of interest on twitter: a first look, "M. Michelson", ACM, 73~80 (2010)
- R. Chen, "The determinants of online customer ratings: a combined domain ontology and topic text analytics approach", Electronic Commerce Research, 17(1), pp.31~50 (2017)
- Z. Qiao, "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 (2017)
- D.M. Blei, "Latent dirichlet allocation", Journal of machine Learning research, 3, pp.993~1022 (2003)
- J. Sleeman, "Advanced Large Scale Cross Domain Temporal Topic Modeling Algorithms to Infer the Influence of Recent Research on IPCC Assessment Reports", Synthesis, 99(81), pp.89~ (2016)
- LDA models for finding trends in technical knowledge domain, "A.P. Shiryaev", IEEE, 551~554 (2017)
- H.S. Choi, "Analyzing research trends in personal information privacy using topic modeling", Computers & Security, 67, pp.244~253 (2017)
- A. Amado, "Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis", European Research on Management and Business Economics, 24, pp.1~7 (2017)
- L. Sun, "Discovering themes and trends in transportation research using topic modeling", Transportation Research Part C: Emerging Technologies, 77, pp.49~66 (2017)
- 문주영, "텍스트마이닝을 이용한 비서학의 지적구조 이해", 비서·사무경영연구, 18(1), pp.83~98 (2009), 한국비서학회
- 조수곤, "텍스트마이닝을 활용한 산업공학 학술지의 논문 주제어간 연관관계 연구", 대한산업공학회지, 38(1), pp.67~73 (2012), 대한산업공학회
- 박자현, "토픽모델링을 활용한 국내 문헌정보학 연구동향 분석", 정보관리학회지, 30(1), pp.7~32 (2013), 한국정보관리학회[DOI]
- R. Mitchell, "Web scraping with Python: collecting data from the modern web", pp.1~256 (2015), O'Reilly Media, Inc
- J. Silge, "Text mining with R: A tidy approach", pp.1~194 (2017), O'Reilly Media, Inc
- 김지은, "텍스트 빅데이터 분석 기법을 활용한 대학구조개혁 평가의 쟁점 분석", 아시아교육연구, 17(3), pp.409~436 (2016), 교육연구소
- "topicmodels: An R package for fitting topic models" (2011)[URL]
- 안정민, "S-헬스케어산업의 경제적 파급효과 분석", 보건의료산업학회지, 11(2), pp.55~64 (2017), 보건의료산업학회