Crowdsourcing and Validating Event-focused Emotion Corpora for German and English

Enrica Troiano, Sebastian Padó, Roman Klinger


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.
Anthology ID:
P19-1391
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4005–4011
Language:
URL:
https://aclanthology.org/P19-1391
DOI:
10.18653/v1/P19-1391
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P19-1391.pdf
Supplementary:
 P19-1391.Supplementary.pdf
Data
Event-focused Emotion Corpora for German and EnglishISEAR