Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.

APPReddit: a Corpus of Reddit Posts Annotated for Appraisal

Marco Antonio Stranisci
First
;
Simona Frenda;Eleonora Ceccaldi;Valerio Basile;Rossana Damiano;Viviana Patti
Last
2022-01-01

Abstract

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.
2022
Language Resources and Evaluation Conference
Marseille, France
June, 2022
Proceedings of the Language Resources and Evaluation Conference
European Language Resources Association
3809
3818
979-10-95546-72-6
http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.406.pdf
Emotion Recognition, Appraisal Theories, Annotated Corpora
Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, Viviana Patti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1870440
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