There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing

Marco Antonio Stranisci
Co-first
Membro del Collaboration Group
;
Rossana Damiano
Membro del Collaboration Group
;
Viviana Patti
Membro del Collaboration Group
;
2024-01-01

Abstract

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.
2024
Empirical Methods in Natural Language Processing
Miami, Florida, US
12-16 novembre
The 2024 Conference on Empirical Methods in Natural Language Processing: Findings of EMNLP 2024
Association for Computational Linguistics
1634
1658
979-8-89176-168-1
https://aclanthology.org/2024.findings-emnlp.89
coping, microaggressions, natural language processing, emotions
Enrica Troiano, Sofie Labat, Marco Antonio Stranisci, Rossana Damiano, Viviana Patti, Roman Klinger
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2034290
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