ORCID
- Shang-Ming Zhou: 0000-0002-0719-9353
Abstract
ObjectivesThe emotional and psychological challenges faced by children with multiple long-term conditions (MLTCs) remain underexplored. This study aimed to analyze sentiments and emotions expressed by this vulnerable population and their caregivers on social media, assess the effects of comorbidities and the COVID-19 pandemic on emotional well-being.MethodsNarratives from the Care Opinion platform (2008–2023) were analyzed by a model called CoEmoBERT, developed using the large language model, distilroberta-base transformer model. The CoEmoBERT-based sentiment analysis categorized emotions into “Positive”, “Negative”, and “Neutral,” with further refinements into specific emotions such as “Sad,” “Fear”, “Satisfied” etc. through pretraining and transferring process. Comorbidity associations with emotions were analyzed. We further examined the impact of the COVID-19 pandemic on patient sentiments and investigated temporal trends in emotional expressions.ResultsOf 389 narratives, 93.8 % reflected negative sentiments, with “Sad” (60.9 %) and “Fear” (15.4 %) being the most prevalent. Negative emotions were linked to severe comorbidities like asthma, cancer, and chronic pain, highlighting the emotional burden of managing MLTCs. Positive sentiments (5.9 %) were associated with effective communication and exceptional healthcare experiences. The analysis revealed strong associations between certain comorbidity combinations and specific emotional responses, with mental health conditions showing the most diverse range of comorbidities and emotional impacts. The COVID-19 pandemic exacerbated negative sentiments, particularly sadness and disgust.ConclusionThis study underscores the significant emotional burden on children with MLTCs, emphasizing the need for integrated care approaches to both physical and emotional well-being. These findings can guide the development of patient-centered care for this population.
Publication Date
2025-06-21
Publication Title
Journal of Affective Disorders
Volume
388
ISSN
0165-0327
Keywords
Affective disorders, Comorbidity, Emotion analysis, Large language model, Multiple long-term conditions, Natural language processing, Pediatric healthcare, Social media
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Oluwalade, T., Ahmadi, H., Huo, L., Sharpe, R., & Zhou, S. (2025) 'Comorbidities and emotions - unpacking the sentiments of pediatric patients with multiple long-term conditions through social media feedback: A large language model-driven study', Journal of Affective Disorders, 388. Available at: 10.1016/j.jad.2025.119752
