@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",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
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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%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
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%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.