Probabilistic emotion and sentiment modelling of patient-reported
  experiences
Curtis Murray Lewis Mitchell Jonathan Tuke Mark Mackay
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.