@inproceedings{stranisci-etal-2022-appreddit,
title = "{APPR}eddit: a Corpus of {R}eddit Posts Annotated for Appraisal",
author = "Stranisci, Marco Antonio and
Frenda, Simona and
Ceccaldi, Eleonora and
Basile, Valerio and
Damiano, Rossana and
Patti, Viviana",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.406",
pages = "3809--3818",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
%A Stranisci, Marco Antonio
%A Frenda, Simona
%A Ceccaldi, Eleonora
%A Basile, Valerio
%A Damiano, Rossana
%A Patti, Viviana
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F stranisci-etal-2022-appreddit
%X 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.
%U https://aclanthology.org/2022.lrec-1.406
%P 3809-3818
Markdown (Informal)
[APPReddit: a Corpus of Reddit Posts Annotated for Appraisal](https://aclanthology.org/2022.lrec-1.406) (Stranisci et al., LREC 2022)
ACL
- Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, and Viviana Patti. 2022. APPReddit: a Corpus of Reddit Posts Annotated for Appraisal. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3809–3818, Marseille, France. European Language Resources Association.