One of the biggest hurdles for the utilization of machine learning in interdisciplinary projects is the need for annotated training data which is costly to create. Emotion annotation is a notoriously difficult task, and the current annotation schemes which are based on psychological theories of human interaction are not always the most conducive for the creation of reliable emotion annotations, nor are they optimal for annotating emotions in the modality of text. This paper discusses the theory, history, and challenges of emotion annotation, and proposes improvements for emotion annotation tasks based on both theory and case studies. These improvements focus on rethinking the categorization of emotions and the overlap and disjointedness of emotion categories.
|ジャーナル||CEUR Workshop Proceedings|
|出版ステータス||Published - 2020|
|イベント||5th Conference Digital Humanities in the Nordic Countries, DHN 2020 - Riga, Latvia|
継続期間: 2020 10 21 → 2020 10 23
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）