@inproceedings{wegge-etal-2022-experiencer,
title = "Experiencer-Specific Emotion and Appraisal Prediction",
author = "Wegge, Maximilian and
Troiano, Enrica and
Oberlaender, Laura Ana Maria and
Klinger, Roman",
editor = "Bamman, David and
Hovy, Dirk and
Jurgens, David and
Keith, Katherine and
O'Connor, Brendan and
Volkova, Svitlana",
booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
month = nov,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlpcss-1.3",
doi = "10.18653/v1/2022.nlpcss-1.3",
pages = "25--32",
abstract = "Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like {``}I felt guilty when he cried{''}, focusing on the sentence level disregards the standpoint of each participant in the situation: the writer ({``}I{''}) and the other entity ({``}he{''}) could in fact have different affective states. The emotions of different entities have been considered only partially in emotion semantic role labeling, a task that relates semantic roles to emotion cue words. Proposing a related task, we narrow the focus on the experiencers of events, and assign an emotion (if any holds) to each of them. To this end, we represent each emotion both categorically and with appraisal variables, as a psychological access to explaining why a person develops a particular emotion. On an event description corpus, our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines, showing that disregarding event participants is an oversimplification for the emotion detection task.",
}
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<abstract>Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like “I felt guilty when he cried”, focusing on the sentence level disregards the standpoint of each participant in the situation: the writer (“I”) and the other entity (“he”) could in fact have different affective states. The emotions of different entities have been considered only partially in emotion semantic role labeling, a task that relates semantic roles to emotion cue words. Proposing a related task, we narrow the focus on the experiencers of events, and assign an emotion (if any holds) to each of them. To this end, we represent each emotion both categorically and with appraisal variables, as a psychological access to explaining why a person develops a particular emotion. On an event description corpus, our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines, showing that disregarding event participants is an oversimplification for the emotion detection task.</abstract>
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%0 Conference Proceedings
%T Experiencer-Specific Emotion and Appraisal Prediction
%A Wegge, Maximilian
%A Troiano, Enrica
%A Oberlaender, Laura Ana Maria
%A Klinger, Roman
%Y Bamman, David
%Y Hovy, Dirk
%Y Jurgens, David
%Y Keith, Katherine
%Y O’Connor, Brendan
%Y Volkova, Svitlana
%S Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wegge-etal-2022-experiencer
%X Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs. With texts like “I felt guilty when he cried”, focusing on the sentence level disregards the standpoint of each participant in the situation: the writer (“I”) and the other entity (“he”) could in fact have different affective states. The emotions of different entities have been considered only partially in emotion semantic role labeling, a task that relates semantic roles to emotion cue words. Proposing a related task, we narrow the focus on the experiencers of events, and assign an emotion (if any holds) to each of them. To this end, we represent each emotion both categorically and with appraisal variables, as a psychological access to explaining why a person develops a particular emotion. On an event description corpus, our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines, showing that disregarding event participants is an oversimplification for the emotion detection task.
%R 10.18653/v1/2022.nlpcss-1.3
%U https://aclanthology.org/2022.nlpcss-1.3
%U https://doi.org/10.18653/v1/2022.nlpcss-1.3
%P 25-32
Markdown (Informal)
[Experiencer-Specific Emotion and Appraisal Prediction](https://aclanthology.org/2022.nlpcss-1.3) (Wegge et al., NLP+CSS 2022)
ACL
- Maximilian Wegge, Enrica Troiano, Laura Ana Maria Oberlaender, and Roman Klinger. 2022. Experiencer-Specific Emotion and Appraisal Prediction. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 25–32, Abu Dhabi, UAE. Association for Computational Linguistics.