# Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback

arXiv: https://arxiv.org/abs/2501.19134 · HTML: https://mlsanity.com/paper/2501.19134/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Fine-Grained Sentiment Dataset for Norwegian](https://mlsanity.com/paper/1911.12722/) (2019)
- [NorBench -- A Benchmark for Norwegian Language Models](https://mlsanity.com/paper/2305.03880/) (2023)
- [Large-Scale Contextualised Language Modelling for Norwegian](https://mlsanity.com/paper/2104.06546/) (2021)
- [NoReC: The Norwegian Review Corpus](https://mlsanity.com/paper/1710.05370/) (2017)
- [It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Comments](https://mlsanity.com/paper/2404.18832/) (2024)

## Built on by (most cited first, 1 of 1)

- [HealthcareNLP: where are we and what is next?](https://mlsanity.com/paper/2512.08617/) (2025)

## Abstract (arXiv)

Sentiment analysis of patient feedback from the public health domain can aid decision makers in evaluating the provided services. The current paper focuses on free-text comments in patient surveys about general practitioners and psychiatric healthcare, annotated with four sentence-level polarity classes -- positive, negative, mixed and neutral -- while also attempting to alleviate data scarcity by leveraging general-domain sources in the form of reviews. For several different architectures, we compare in-domain and out-of-domain effects, as well as the effects of training joint multi-domain models.