Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback

Fuente: arXiv
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Main Authors: Rønningstad, Egil, Storset, Lilja Charlotte, Mæhlum, Petter, Øvrelid, Lilja, Velldal, Erik
Format: Preprint
Published: 2025
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author Rønningstad, Egil
Storset, Lilja Charlotte
Mæhlum, Petter
Øvrelid, Lilja
Velldal, Erik
author_facet Rønningstad, Egil
Storset, Lilja Charlotte
Mæhlum, Petter
Øvrelid, Lilja
Velldal, Erik
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2501_19134
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
Rønningstad, Egil
Storset, Lilja Charlotte
Mæhlum, Petter
Øvrelid, Lilja
Velldal, Erik
Computation and Language
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.
title Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback
topic Computation and Language
url https://arxiv.org/abs/2501.19134