Lilja Charlotte Storset

• Norman, Rebecka Maria; Storset, Lilja Charlotte; Mæhlum, Petter; Iversen, Hilde Karin Hestad; Jelin, Elma & Velldal, Erik [Vis alle 8 forfattere av denne artikkelen] (2026). Exploiting survey and online patient experience comments in general practice: validating a fine-tuned language model for automatic sentiment analysis. Family Practice. ISSN 0263-2136. 43(5). doi: 10.1093/fampra/cmag071. Fulltekst i vitenarkiv
• Storset, Lilja Charlotte; Jelin, Elma; Norman, Rebecka Maria; Bjertnæs, Øyvind Andresen; Øvrelid, Lilja & Velldal, Erik (2026). From Pain to Praise: Aspect-Based Sentiment Analysis for Norwegian Patient Feedback. I Danilova, Vera; Kurfalı, Murathan; Söderfeldt, Ylva; Reed, Julia & Burchell, Andrew (Red.), Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026). Association for Computational Linguistics (ACL). ISSN 9798891763678. s. 191–202. doi: 10.18653/v1/2026.healing-1.16. Fulltekst i vitenarkiv Vis sammendrag

  This paper describes a new dataset for aspect-based sentiment analysis (ABSA) for analyzing patient feedback about healthcare services. In an interdisciplinary collaboration spanning the fields of natural language processing and healthcare research, we manually annotate a dataset of 2382 free-text comments collected from national patient experience surveys in Norway, covering two sub-fields of services – special mental healthcare and general practitioners. Annotations are provided on both the sentence- and comment-level, covering a fine-grained set of 25 unique healthcare-related aspects and their polarities. We also report results for fine-tuning both encoder- and decoder models on the resulting dataset, comparing different modeling strategies, like joint and sequential prediction of aspects and polarity. The resources developed in this work can assist healthcare researchers in the analysis of patient feedback, bringing a much more efficient approach compared to today’s manual analysis, potentially leading to improved patient satisfaction and clinical outcomes.

• Jelin, Elma; Storset, Lilja Charlotte; Norman, Rebecka Maria; Iversen, Hilde Karin Hestad; Ellingsen-Dalskau, Lina Harvold & Mæhlum, Petter [Vis alle 9 forfattere av denne artikkelen] (2025). From words to action? A scoping review on automatic sentiment analysis of patient experience comments from online sources and surveys. BMJ Health & Care Informatics. 32(1). doi: 10.1136/bmjhci-2025-101631. Fulltekst i vitenarkiv Vis sammendrag

  Background Automatic analysis of free-text patient comments enables the efficient processing of large feedback volumes, reducing reliance on manual review. A 2021 review examined natural language processing (NLP) and sentiment analysis (SA) in patient experience research; however, recent advances in deep learning and generative artificial intelligence (AI) call for an updated synthesis. Objectives This scoping review aims to map and summarise recent studies applying SA to unstructured patient experience data related to healthcare services. Methods Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a comprehensive search across Medline, CINAHL, Web of Science, Cochrane, Embase and APA PsycINFO. We included studies published from January 2020 to March 2024 in English or a Scandinavian language. Eligible studies analysed patient feedback using NLP techniques and described the development or validation of SA models. Two reviewers independently screened the studies and extracted data, which were presented in tables both tabular and narrative forms. Results 30 studies were included, primarily from the USA, Europe and Asia. Patient comments were mostly sourced from online platforms such as social media. Feedback largely concerned hospital care. 18 studies employed rule-based SA approaches, while 12 applied supervised machine learning (ML) and only 4 studies used deep learning models. Few addressed the visualisation or practical applications in healthcare. Conclusion Despite significant progress, modern methods like deep learning and generative AI remain underused in SA of patient-experience data. Limited focus on implementation restricts SA’s role in quality improvement. Future research should assess advanced methods and their cost-effectiveness versus traditional ML.

• Storset, Lilja Charlotte; Rønningstad, Egil; Mæhlum, Petter; Velldal, Erik & Øvrelid, Lilja (2025). Mixed Feelings: Cross-Domain Sentiment Classification of Patient Feedback. I Johansson, Richard & Stymne, Sara (Red.), Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025). University of Tartu Library. ISSN 9789908531090. s. 537–543. doi: https:/aclanthology.org/2025.nodalida-1.58/. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Touileb, Samia; Murstad, Jeanett; Mæhlum, Petter; Steskal, Lubos; Storset, Lilja Charlotte & You, Huiling [Vis alle 7 forfattere av denne artikkelen] (2024). EDEN: A Dataset for Event Detection in Norwegian News. I Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani & Xue, Nianwen (Red.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). European Language Resources Association. ISSN 9782493814104. s. 5495–5506. Fulltekst i vitenarkiv Vis sammendrag

  We present EDEN, the first Norwegian dataset annotated with event information at the sentence level, adapting the widely used ACE event schema to Norwegian. The paper describes the manual annotation of Norwegian text as well as transcribed speech in the news domain, together with inter-annotator agreement and discussions of relevant dataset statistics. We also present preliminary modeling results using a graph-based event parser. The resulting dataset will be made freely available for download and use.

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