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Employing large language models for emotion detection in psychotherapy transcripts

Bibliographic Data

ID15520534
AuthorsChristopher Lalk (0000-0002-1793-3507, Osnabrück University, corresponding author), Kim Targan (Osnabrück University), Tobias Steinbrenner (0009-0005-0434-2870, Osnabrück University), Jana Schaffrath (0000-0003-0106-0243, Universität Trier), Steffen T Eberhardt (0000-0002-9900-4671, Universität Trier), Steffen Eberhardt, Brian Schwartz (0000-0003-4695-4953, Universität Trier), Antonia Vehlen (0000-0002-6019-3161, Universität Trier), Wolfgang Lutz (0000-0001-7975-8145, Universität Trier), Julian A Rubel (0000-0002-9625-6611, Osnabrück University), Julian Rubel
Year2025
Volume16
Pages1504306-1504306
Publication date2025-05-09
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2025.1504306
PMID40417271
OpenAlexW4410247794
LanguageEN
References cited85

Even though the classification results were only moderate, our model achieved a good performance especially for prediction of symptom severity. The results confirm the role of negative emotions in the prediction of symptom severity, while they also highlight the role of positive emotions in fostering a good alliance. Future directions entail the improvement of the labeled dataset, especially with regards to domain-specificity and incorporating context information. Additionally, other modalities and Natural Language Processsing (NLP)-based alliance assessment could be integrated

Alliance · Anger · Context (archaeology · German · Psychotherapist · Surprise · Clinical Psychology · Mental Health Research Topics · Mental Health via Writing · Personality Disorders and Psychopathology · Psychology · Social Psychology

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