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Conference Paper

Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text

MPS-Authors
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Volkova,  EP
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Mohler,  BJ
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://dl.acm.org/doi/10.5555/1860631.1860643
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Citation

Volkova, E., Mohler, B., Meurers, D., Gerdemann, D., & Bülthoff, H. (2010). Emotional Perception of Fairy Tales: Achieving Agreement in Emotion Annotation of Text. In D. Inkpen, & C. Strapparava (Eds.), CAAGET '10: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (pp. 98-106). Morristown, NJ, USA: Association for Computational Linguistics.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BF9C-E
Abstract
Emotion analysis (EA) is a rapidly developing area in computational linguistics. An EA system can be extremely useful in fields such as information retrieval and emotion-driven computer animation. For most EA systems, the number of emotion classes is very limited and the text units the classes are assigned to are discrete and predefined. The question we address in this paper is whether the set of emotion categories can be enriched and whether the units to which the categories are assigned can be more flexibly defined. We present an experiment showing how an annotation task can be set up so that untrained participants can perform emotion analysis with high agreement even when not restricted to a predetermined annotation unit and using a rich set of emotion categories. As such it sets the stage for the development of more complex EA systems which are closer to the actual human emotional perception of text.