@inproceedings{nunnari-etal-2026-sentiment,
title = "Sentiment Analysis of {G}erman {S}ign {L}anguage Fairy Tales",
author = "Nunnari, Fabrizio and
Jain, Siddhant and
Gebhard, Patrick",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.748/",
doi = "10.63317/3cyfzw6vs9oe",
pages = "9525--9534",
abstract = "We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels of valence (negative, neutral, positive) on German fairy tales text segments using four large language models (LLMs) and majority voting, reaching an inter-annotator agreement of 0.781 Krippendorff{'}s alpha. Second, we extract face and body motion features from each corresponding DGS video segment using MediaPipe. Finally, we train an explainable model (based on XGBoost) to predict negative, neutral or positive sentiment from video features. Results show an average balanced accuracy of 0.631. A thorough analysis of the most important features reveal that, in addition to eyebrows and mouth motion on the face, also the motion of hips, elbows, and shoulders considerably contribute in the discrimination of the conveyed sentiment, indicating an equal importance of face and body for sentiment communication in sign language."
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<abstract>We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels of valence (negative, neutral, positive) on German fairy tales text segments using four large language models (LLMs) and majority voting, reaching an inter-annotator agreement of 0.781 Krippendorff’s alpha. Second, we extract face and body motion features from each corresponding DGS video segment using MediaPipe. Finally, we train an explainable model (based on XGBoost) to predict negative, neutral or positive sentiment from video features. Results show an average balanced accuracy of 0.631. A thorough analysis of the most important features reveal that, in addition to eyebrows and mouth motion on the face, also the motion of hips, elbows, and shoulders considerably contribute in the discrimination of the conveyed sentiment, indicating an equal importance of face and body for sentiment communication in sign language.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis of German Sign Language Fairy Tales
%A Nunnari, Fabrizio
%A Jain, Siddhant
%A Gebhard, Patrick
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F nunnari-etal-2026-sentiment
%X We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels of valence (negative, neutral, positive) on German fairy tales text segments using four large language models (LLMs) and majority voting, reaching an inter-annotator agreement of 0.781 Krippendorff’s alpha. Second, we extract face and body motion features from each corresponding DGS video segment using MediaPipe. Finally, we train an explainable model (based on XGBoost) to predict negative, neutral or positive sentiment from video features. Results show an average balanced accuracy of 0.631. A thorough analysis of the most important features reveal that, in addition to eyebrows and mouth motion on the face, also the motion of hips, elbows, and shoulders considerably contribute in the discrimination of the conveyed sentiment, indicating an equal importance of face and body for sentiment communication in sign language.
%R 10.63317/3cyfzw6vs9oe
%U https://aclanthology.org/2026.lrec-1.748/
%U https://doi.org/10.63317/3cyfzw6vs9oe
%P 9525-9534
Markdown (Informal)
[Sentiment Analysis of German Sign Language Fairy Tales](https://aclanthology.org/2026.lrec-1.748/) (Nunnari et al., LREC 2026)
ACL
- Fabrizio Nunnari, Siddhant Jain, and Patrick Gebhard. 2026. Sentiment Analysis of German Sign Language Fairy Tales. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9525–9534, Palma de Mallorca, Spain. ELRA Language Resource Association.