[논문]텍스트마이닝을 활용한 노인 헬스케어 앱 사용 추이 및 동향 분석

변현; 전상완; 이은석

doi:10.15207/jkcs.2022.13.05.313

[국내논문] 텍스트마이닝을 활용한 노인 헬스케어 앱 사용 추이 및 동향 분석
A Study on the Current Situation and Trend Analysis of The Elderly Healthcare Applications Using Big Data Analysis 원문보기

한국융합학회논문지 = Journal of the Korea Convergence Society, v.13 no.5, 2022년, pp.313 - 325

변현 (가천대학교 운동재활융합연구소) , 전상완 (가천대학교 운동재활융합연구소) , 이은석 (가천대학교 운동재활학과)

초록
AI-Helper

본 연구의 목적은 노인 헬스케어앱 시장의 변화 추이를 텍스트 마이닝 분석을 통해 살펴보고 노인 헬스케어앱 활성화를 위한 기초자료를 제시하고자 한다. 데이터 수집은 네이버, 다음, 블로그 웹, 까페를 대상으로 이루어졌으며, 연구방법은 빅데이터 분석 프로그램인 텍스톰(Textom)과 Ucinet6를 이용하여 텍스트마이닝, TF-IDF(Term frequency-inverse document frequency), 감성분석, 의미연결망분석을 실시하였다. 워드 클라우드를 실시한 결과 빈도 순으로 현장교육, 헬스케어, 전신재활운동기구, 서비스, 운동 등으로 나타났으며, TF-IDF 순위로는 현장교육, 헬스케어, 재활운동기구, 서비스, 건강 순으로 나타났다. 노인 스포츠 어플리케이션에 대한 감성분석을 실시한 결과 긍정비율로 81.3%, 부정비율이 18.7%로 나타났으며, 헬스케어앱 정보격차 해소, 융복합 헬스케어기술, 확산매체, 노인헬스케어앱 산업, 사회적 배경, 콘텐츠로 총 6개의 범주가 최종적으로 도출되었다. 결론적으로 노인 헬스케어앱이 노인들에게 수용 및 활용되기 위해 확산 인프라가 잘 갖추어져 있어야 하며, 융복합 기술의 적극적인 도입과 노인도 쉽게 사용할 수 있는 콘텐츠 개발을 통해 헬스케어 앱의 효과를 극대화하여야 한다.

Abstract ▼ AI-Helper

The purpose of this study is to examine the changes in the elderly healthcare app market through text mining analysis and to present basic data for activating elderly healthcare apps. Data collection was conducted on Naver, Daum, blog web, and cafe. As for the research method, text mining, TF-IDF(Term frequency-inverse document frequency), emotional analysis, and semantic network analysis were conducted using Textom and Ucinet6, which are big data analysis programs. As a result of this study, a total of six categories were finally derived: resolving the healthcare app information gap, convergence healthcare technology, diffusion media, elderly healthcare app industry, social background, and content. In conclusion, in order for elderly healthcare apps to be accepted and utilized by the elderly, they must have a good diffusion infrastructure, and the effectiveness of healthcare apps must be maximized through the active introduction of convergence technology and content development that can be easily used by the elderly.

Keyword

표/그림 (9)

표 Table 1. Analysis data information
표 Table 2. Data preprocessing information
그림 Fig. 1. Word Cloud
표 Table 3. Text Frequency Analysis with the keyword
그림 Fig. 2. Social-network of Healthcare app
표 Table 4. Sentiment analysis result
표 Table 5. CONCOR analysis results
그림 Fig. 3. Social-network of concor analysis
표 Table 6. Categorization result

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