텍스트마이닝을 통한 노인장기요양보험 키워드 네트워크 분석

Abstract

Objectives This study conducted research using big data in order to overcome the limitations of existing qualitative research or analysis research. By analyzing keywords, the flow and role of long-term care insurance in society were analyzed. Methods Issues were searched through text mining, one of the big data techniques, and the flow of agendas by period was examined by 3 time points (institutional settlement period, 1st basic plan, 2nd basic plan). Using R and NetMiner, Daum News (news.daum.net) and Naver News (news.naver.com) were web-scraped to collect 20,965 news articles, 4,994 articles were filtered for keyword extraction and analysis. Result: Looking at the characteristics of each data type, in all data types, long-term care institutions (including nursing homes) and care providers appear as the top keywords, and the keyword subgroup characteristics are ① grade/service, ② institution management, and ③ the employee group includes the keyword subgroup. Conclusions This study is based on the subject of long-term care insurance for the elderly and applies big data analysis techniques, and can be used as a decision-making tool in establishing policies and systems.