Emotion annotation: Rethinking emotion categorization

Emily Öhman*


研究成果: Conference article査読


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 212020 10 23

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)


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