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        <title>Crowdsourcing and Validating Event-focused Emotion Corpora for German and English</title>
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    <abstract>Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer’s appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.</abstract>
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%0 Conference Proceedings
%T Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
%A Troiano, Enrica
%A Padó, Sebastian
%A Klinger, Roman
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F troiano-etal-2019-crowdsourcing
%X Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer’s appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.
%R 10.18653/v1/P19-1391
%U https://aclanthology.org/P19-1391
%U https://doi.org/10.18653/v1/P19-1391
%P 4005-4011

Markdown (Informal)

[Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://aclanthology.org/P19-1391) (Troiano et al., ACL 2019)

• Crowdsourcing and Validating Event-focused Emotion Corpora for German and English (Troiano et al., ACL 2019)

ACL

• Enrica Troiano, Sebastian Padó, and Roman Klinger. 2019. Crowdsourcing and Validating Event-focused Emotion Corpora for German and English. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4005–4011, Florence, Italy. Association for Computational Linguistics.