Sentiment analysis has a range of corpora available across multiple
languages. For emotion analysis, the situation is more limited,
which hinders potential research on cross-lingual 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.
%0 Conference Paper
%1 troiano19:_crowd_valid_event_emotion_corpor_german_englis
%A Troiano, Enrica
%A Padó, Sebastian
%A Klinger, Roman
%B Proceedings of ACL
%C Florence, Italy
%D 2019
%K conference imported myown
%T Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
%U https://aclweb.org/anthology/papers/P/P19/P19-1391/
%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 cross-lingual 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.
@inproceedings{troiano19:_crowd_valid_event_emotion_corpor_german_englis,
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 cross-lingual 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.},
added-at = {2019-05-14T12:02:03.000+0200},
address = {Florence, Italy},
author = {Troiano, Enrica and Padó, Sebastian and Klinger, Roman},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22d970d8349c922f2fbe1d6e9c6777653/sp},
booktitle = {Proceedings of ACL},
interhash = {fab51468c8718846349885d5aa327f91},
intrahash = {2d970d8349c922f2fbe1d6e9c6777653},
keywords = {conference imported myown},
timestamp = {2019-07-29T19:48:43.000+0200},
title = {Crowdsourcing and Validating Event-focused Emotion Corpora for German and English},
url = {https://aclweb.org/anthology/papers/P/P19/P19-1391/},
year = 2019
}