@inproceedings{ronningstad-etal-2025-mixed,
    title = "Mixed Feelings: {Cross-Domain} Sentiment Classification of Patient Feedback",
    author = "R{\o}nningstad, Egil  and
      Storset, Lilja Charlotte  and
      M{\ae}hlum, Petter  and
      {\O}vrelid, Lilja  and
      Velldal, Erik",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/moar-dois/2025.nodalida-1.58/",
    pages = "537--543",
    ISBN = "978-9908-53-109-0",
    abstract = "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."
}