Appraisal Theories for Emotion Classification in Text

Jan Hofmann, Enrica Troiano, Kai Sassenberg, Roman Klinger


Abstract
Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid.”). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.
Anthology ID:
2020.coling-main.11
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
125–138
Language:
URL:
https://aclanthology.org/2020.coling-main.11
DOI:
10.18653/v1/2020.coling-main.11
Bibkey:
Cite (ACL):
Jan Hofmann, Enrica Troiano, Kai Sassenberg, and Roman Klinger. 2020. Appraisal Theories for Emotion Classification in Text. In Proceedings of the 28th International Conference on Computational Linguistics, pages 125–138, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Appraisal Theories for Emotion Classification in Text (Hofmann et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.11.pdf