  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.
