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