mlsanityData to 14 Oct 2026

Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback

arXiv 2501.19134 ↗First posted 31 Jan 2025cs.CLcited by 1

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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.

Key facts. Cited by 1 paper 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.

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6 of 9 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019A Fine-Grained Sentiment Dataset for Norwegiancited by 23
  3. 2023NorBench -- A Benchmark for Norwegian Language Modelscited by 23
  4. 2021Large-Scale Contextualised Language Modelling for Norwegiancited by 21
  5. 2017NoReC: The Norwegian Review Corpuscited by 17
  6. 2024It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Commentscited by 4

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Citing papers per year: 2025: 1.

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