@inproceedings{ohman-etal-2020-xed,
title = "{XED}: A Multilingual Dataset for Sentiment Analysis and Emotion Detection",
author = {{\"O}hman, Emily and
P{\`a}mies, Marc and
Kajava, Kaisla and
Tiedemann, J{\"o}rg},
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.575",
doi = "10.18653/v1/2020.coling-main.575",
pages = "6542--6552",
abstract = "We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik{'}s core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.",
}
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%0 Conference Proceedings
%T XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection
%A Öhman, Emily
%A Pàmies, Marc
%A Kajava, Kaisla
%A Tiedemann, Jörg
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ohman-etal-2020-xed
%X We introduce XED, a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages. We use Plutchik’s core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset. The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.
%R 10.18653/v1/2020.coling-main.575
%U https://aclanthology.org/2020.coling-main.575
%U https://doi.org/10.18653/v1/2020.coling-main.575
%P 6542-6552
Markdown (Informal)
[XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://aclanthology.org/2020.coling-main.575) (Öhman et al., COLING 2020)
ACL