Sentiment Analysis of German Sign Language Fairy Tales
arXiv cs.CL / 4/20/2026
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Key Points
- The paper introduces a new dataset and study aimed at sentiment analysis of German Sign Language (DGS) fairy tales across negative, neutral, and positive valence categories.
- It uses four large language models (LLMs) with majority voting to perform sentiment labeling on German text segments, achieving an inter-annotator agreement of 0.781 (Krippendorff’s alpha).
- The work extracts facial and body motion features from DGS video segments using MediaPipe and then trains an explainable XGBoost model to predict sentiment from those video-based features.
- The explainable analysis finds that, beyond facial cues such as eyebrow and mouth motion, body motions—including hips, elbows, and shoulders—also play a major role, suggesting both face and body are equally important for conveying sentiment in sign language.
- The video-feature sentiment prediction model achieves an average balanced accuracy of 0.631, indicating promising but still moderate performance for this task.
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