질적연구 진술문을 이용한 중환자실 생존자의 감성분석 (Sentiment Analysis of the Quotations of Intensive Care Unit Survivors in Qualitative Studies)

서지정보

· 발행기관 : 한국중환자간호학회
· 수록지 정보 : 중환자간호학회지 / 11권 / 1호 / 1 ~ 14페이지
· 저자명 : 강지연

초록

Purpose : As the intensive care unit (ICU) survival rate increases, interest in the lives of ICU survivors has also been increasing. The purpose of this study was to identify the sentiment of ICU survivors. Method : The author analyzed the quotations from previous qualitative studies related to ICU survivors; a total of 1,074 sentences comprising 429 quotations from 25 relevant studies were analyzed. A word cloud created in the R program was utilized to identify the most frequent adjectives used, and sentiment and emotional scores were calculated using the Artificial Intelligence (AI) program. Results : The 10 adjectives that appeared the most in the quotations were ‘difficult’, ‘different’, ‘normal’, ‘able’, ‘hard’, ‘bad’, ‘ill’, ‘better’, ‘weak’, and ‘afraid’, in order of decreasing occurrence. The mean sentiment score was negative (-.31±.23), and the three emotions with the highest score were ‘sadness’(.52±.13), ‘joy’(.35±.22), and ‘fear’(.30±.25). Conclusion : The natural language processing of AI used in this study is a relatively new method. As such, it is necessary to refine the methodology through repeated research in various nursing fields. In addition, further studies on nursing interventions that improve the coherency of ICU memory of survivors and familial support for the ICU survivors are needed.

영어초록

Purpose : As the intensive care unit (ICU) survival rate increases, interest in the lives of ICU survivors has also been increasing. The purpose of this study was to identify the sentiment of ICU survivors. Method : The author analyzed the quotations from previous qualitative studies related to ICU survivors; a total of 1,074 sentences comprising 429 quotations from 25 relevant studies were analyzed. A word cloud created in the R program was utilized to identify the most frequent adjectives used, and sentiment and emotional scores were calculated using the Artificial Intelligence (AI) program. Results : The 10 adjectives that appeared the most in the quotations were ‘difficult’, ‘different’, ‘normal’, ‘able’, ‘hard’, ‘bad’, ‘ill’, ‘better’, ‘weak’, and ‘afraid’, in order of decreasing occurrence. The mean sentiment score was negative (-.31±.23), and the three emotions with the highest score were ‘sadness’(.52±.13), ‘joy’(.35±.22), and ‘fear’(.30±.25). Conclusion : The natural language processing of AI used in this study is a relatively new method. As such, it is necessary to refine the methodology through repeated research in various nursing fields. In addition, further studies on nursing interventions that improve the coherency of ICU memory of survivors and familial support for the ICU survivors are needed.

참고자료

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