[논문]텍스트 마이닝 분석을 통한 노인학대 관련 연구 동향 분석 : 2004년~2021년까지 발행된 국내 학술논문을 중심으로

윤기혁

doi:10.20465/kiots.2022.8.4.025

[국내논문] 텍스트 마이닝 분석을 통한 노인학대 관련 연구 동향 분석 : 2004년~2021년까지 발행된 국내 학술논문을 중심으로
Analysis of Research Trends in Elder Abuse Using Text Mining : Academic Papers from 2004 to 2021. 원문보기

사물인터넷융복합논문지 = Journal of internet of things and convergence, v.8 no.4, 2022년, pp.25 - 40

윤기혁 (동명대학교 사회복지학과)

초록
AI-Helper

본 연구는 초고령화사회 진입을 목전에 두고 있는 우리나라에서 지속적으로 증가하고 있는 노인학대 학술 연구 동향을 파악하기 위해서 텍스트 마이닝 기법을 활용하였다. 분석 자료는 노인보호전문기관이 설립된 2004년부터 2021년까지 18년간 국내 전문학술지에 게재된 노인학대 관련 학술논문의 제목, 주제어, 초록을 텍스트로 전환하고, 분석 시기는 3개 구간으로 세분화 하여 논문의 패턴 및 전체 데이터 속에 의미를 파악하였다. 연구 결과를 요약하면 다음과 같다. 첫째, 본 연구에서 총 249편의 논문이 선정되었고(1구간은 81편, 2구간 64편, 3구간은 104편이 논문이 각각 선정). 연 평균 13.8편으로 2014년 이후 꾸준히 증가 후 2020년부터 연 평균이하로 감소하고 있다. 둘째 노인학대 텍스트 마이닝 결과 i) 상위 주요 키워드인 단어 빈도분석 결과 모든 구간(2004년~2021년)에 공통적으로 나타난 키워드는 노인학대, 노인, 영향, 요인, 인식, 가족, 사회, 방안, 경험, 학대피해노인, 학대예방, 우울 등이다. ii) TF-IDF 분석 결과 모든 구간에 공통적으로 출현한 키워드는 영향, 인식, 사회, 방안, 학대예방, 경험, 우울 등으로 나타났고, iii) 연결중심성 분석 결과 전 구간에 공통적으로 출현한 키워드는 노인학대, 노인, 영향, 요인, 특성, 인식, 가족, 방안, 사회, 학대예방, 경험 등이다. 셋째, CONCOR 분석 결과 1구간은 5개의 군집으로, 2구간은 7개의 군집으로, 3구간은 6개의 군집으로 각각 나타났다. 상기의 분석 결과 등을 바탕으로 노인학대 학술 연구의 동향을 살펴보았고, 이를 바탕으로 향후 노인학대 학술 연구를 위한 다양한 제언을 제시하였다.

Abstract ▼ AI-Helper

This study aimed to understand the increasing number of elder abuses in South Korea, where entry into the super-aged society is imminent, by implementing text mining analysis. Korean Academic journals were obtained from 2004, the establishment year of the senior care agency, to 2021. We performed natural language processing of the titles, keywords, and abstracts and divided them into three segments of periods to identify latent meanings in the data. The results illustrated that the first section included 81 papers, the second 64, and the third 104 respectively, averaging 13.8 annually, which increased its numbers from 2014 until the decrease below the annual average in 2020. Word frequency demonstrated that the common keywords of the entire segments were 'elder abuse,' 'elders,' 'influences,' 'factors,' 'recognition,' 'family,' 'society,' 'prevention plans,' 'experiences,' 'abused elders,' 'abuse prevention,' 'depression,' etc., in consecutive order. TF-IDF indicated that 'influences,' 'recognition,' 'society,' 'prevention plans,' 'abuse prevention,' 'experiences,' 'depression,' etc., were the common keywords of all divisions. Network text analysis displayed that the commonly represented keywords were 'elder abuse,' 'elders,' 'influences,' 'factors,' 'characteristics,' 'recognition,' 'family,' 'prevention plans,' 'society,' 'abuse prevention,' and 'experiences' in the entire sections. concor analysis presented that the first segment consisted of 5 groups, the second 7, and the third 6. We suggest future directions for elder abuse research based on the results.

주제어

표/그림 (10)

그림 [Fig. 1] word cloud
표 Frequency to Rank 30 of Keyword
표 TF-IDF to Rank 30 of Keyword
표 Degree Centrality to Rank 30 of Keyword
그림 [Fig. 2] 1 section - CONCOR Analysis
그림 [Fig. 3] 2 section - CONCOR Analysis
표 1 section - CONCOR Analysis
표 2 section - CONCOR Analysis
그림 [Fig. 4] 3 section - CONCOR Analysis
표 3 section - CONCOR Analysis

참고문헌 (59)

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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