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

## TL;DR

This paper introduces deISEAR, a German emotion corpus created through crowdsourcing, and demonstrates that English-to-German transfer learning for emotion classification maintains performance.

## Contribution

It develops the first German emotion corpus aligned with ISEAR and shows effective cross-lingual transfer using machine translation.

## Key findings

- deISEAR corpus created for German emotion analysis
- Cross-lingual transfer from English to German maintains classification performance
- Crowdsourcing effectively gathers emotion descriptions and annotations

## 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.