# Probabilistic emotion and sentiment modelling of patient-reported experiences

arXiv: https://arxiv.org/abs/2401.04367 · HTML: https://mlsanity.com/paper/2401.04367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Bias in Emotion Recognition with ChatGPT](https://mlsanity.com/paper/2310.11753/) (2023)
- [Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit](https://mlsanity.com/paper/2005.10454/) (2020)
- [Multilayer Networks for Text Analysis with Multiple Data Types](https://mlsanity.com/paper/2106.15821/) (2021)
- [A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter](https://mlsanity.com/paper/1805.09959/) (2018)
- [Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework](https://mlsanity.com/paper/2210.04232/) (2022)

## Abstract (arXiv)

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.