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Perception and sentiment analysis of palliative care in Chinese social media : qualitative studies based on machine learning
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BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative care.
AIM: To explore the current situation of the public's perception and sentiment towards palliative care and reduce the barriers health-related persons face in providing professional services.
METHOD: The research steps include text acquisition, text cleaning, data standardization, K-Means clustering algorithm, and sentiment analysis algorithm.
RESULTS: This study had 9017 comments. The comments increased yearly from 2014 to 2023. K-Means clustering results showed patients' physical condition, disease knowledge, and nursing service. Boson NLP results showed 3264 negative comments, 3451 positive comments, and 2302 neutral objective comments. The dictionary method showed positive and negative emotions such as anger, disgust, fear, sad, surprise, good, and happy. Negative emotions were mainly in Physical and mental condition. Positive emotions were mainly in nursing service and unrelated to disease knowledge.
CONCLUSION: Healthcare professionals should pay attention to the adverse effects of public misperceptions and negative emotions. They provide appropriate measures to enhance positive emotions and perceptions and encourage patients to accept palliative care.
http://dx.doi.org/10.1016/j.socscimed.2025.118178
Voir la revue «SOCIAL SCIENCE & MEDICINE, 379»
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