J Korean Crit Care Nurs.  2018 Feb;11(1):1-14. 10.0000/jkccn.2018.11.1.1.

Sentiment Analysis of the Quotations of Intensive Care Unit Survivors in Qualitative Studies

Affiliations
  • 1Professor, Department of Nursing, Dong-A University, Korea. jykang@dau.ac.kr

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.

Keyword

Artificial intelligence; Critical illness; Emotions; Intensive care units; Survivors

MeSH Terms

Artificial Intelligence
Critical Care*
Critical Illness
Humans
Intensive Care Units*
Memory
Methods
Natural Language Processing
Nursing
Survival Rate
Survivors
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