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토픽모델링과 시계열 회귀분석을 활용한 헬스케어 분야의 뉴스 빅데이터 분석 연구
Big Data News Analysis in Healthcare Using Topic Modeling and Time Series Regression Analysis
- 김은정;
- 장석권;
- 이상용
초록
This research aims to identify key initiatives and a policy approach to support the industrialization of the sector. The research collected a total of 91,873 news data points relating to healthcare between 2013 to 2022. A total of 20 topics were derived through topic modeling analysis, and as a result of time series regression analysis, 4 hot topics (Healthcare, Biopharmaceuticals, Corporate outlookㆍSales, GovernmentㆍPolicy), 3 cold topics (Smart devices, StocksㆍInvestment, Urban developmentㆍConstruction) derived a significant topic. The research findings will serve as an important data source for government institutions that are engaged in the formulation and implementation of Korea’s policies.
키워드
디지털 헬스케어; 토픽모델링; LDA; 시계열회귀분석; 데이터마이닝; Digital healthcare; Topic Modeling; LDA; Time Series Regression; Data Mining
- 제목
- 토픽모델링과 시계열 회귀분석을 활용한 헬스케어 분야의 뉴스 빅데이터 분석 연구
- 제목 (타언어)
- Big Data News Analysis in Healthcare Using Topic Modeling and Time Series Regression Analysis
- 저자
- 김은정; 장석권; 이상용
- 발행일
- 2023-08
- 저널명
- 경영정보학연구
- 권
- 25
- 호
- 3
- 페이지
- 163 ~ 177