Sentiment Analysis of the Quotations of Intensive Care Unit Survivors in Qualitative Studies
- Author:
Jiyeon KANG
1
Author Information
1. Professor, Department of Nursing, Dong-A University, Korea. jykang@dau.ac.kr
- Publication Type:Original Article
- Keywords:
Artificial intelligence;
Critical illness;
Emotions;
Intensive care units;
Survivors
- MeSH:
Artificial Intelligence;
Critical Care;
Critical Illness;
Humans;
Intensive Care Units;
Memory;
Methods;
Natural Language Processing;
Nursing;
Survival Rate;
Survivors
- From:
Journal of Korean Critical Care Nursing
2018;11(1):1-14
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
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.
