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

TL;DR

This paper introduces a novel corpus and task for identifying coping strategies in text, aiming to bridge the gap between psychological theories of emotion and computational NLP approaches.

Contribution

It presents a new role-playing based corpus and the task of coping identification to explore emotion mechanisms in language, which is under-explored in NLP.

Findings

Coping strategies are expressed in text but are challenging to recognize.

Humans and models both struggle to accurately identify coping strategies.

The work opens new avenues for modeling emotion mechanisms in NLP.

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.