Crowdsourcing and Validating Event-focused Emotion Corpora for German and English
This addresses the problem of limited cross-lingual emotion analysis resources for researchers, though it is incremental as it adapts an existing dataset design.
The paper tackled the lack of emotion analysis corpora for German by constructing deISEAR, a crowdsourced dataset analogous to the English ISEAR, and showed that transferring an emotion classification model from English to German via machine translation does not cause a performance drop on average.
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.