Between accuracy and empathy: A comparative study of human and transformer-based multi-label emotion annotation
This study examines structural alignment between human and transformer-based multi-label emotion annotation in a low-resource language setting. A corpus of 408 Serbian narratives was manually annotated using soft-label scores mapped to the 28-category GoEmotions taxonomy. Inter-annotator agreement was moderate (micro...