Probabilistic emotion and sentiment modelling of patient-reported experiences

• URL: http://arxiv.org/abs/2401.04367v1
• Date: Tue, 9 Jan 2024 05:39:20 GMT
• Title: Probabilistic emotion and sentiment modelling of patient-reported experiences
• Authors: Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay
• Abstract summary: This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employ metadata network topic modelling to analyse patient-reported experiences from Care Opinion. We develop a probabilistic, context-specific emotion recommender system capable of predicting both multilabel emotions and binary sentiments.
• Score: 0.04096453902709291
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-reported experiences from Care Opinion, revealing key emotional themes linked to patient-caregiver interactions and clinical outcomes. We develop a probabilistic, context-specific emotion recommender system capable of predicting both multilabel emotions and binary sentiments using a naive Bayes classifier using contextually meaningful topics as predictors. The superior performance of our predicted emotions under this model compared to baseline models was assessed using the information retrieval metrics nDCG and Q-measure, and our predicted sentiments achieved an F1 score of 0.921, significantly outperforming standard sentiment lexicons. This method offers a transparent, cost-effective way to understand patient feedback, enhancing traditional collection methods and informing individualised patient care. Our findings are accessible via an R package and interactive dashboard, providing valuable tools for healthcare researchers and practitioners.

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