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

arXiv: https://arxiv.org/abs/1905.13618 · HTML: https://mlsanity.com/paper/1905.13618/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 25 of 25)

- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Few-Shot Learning with Siamese Networks and Label Tuning](https://mlsanity.com/paper/2203.14655/) (2022)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [Emotion Recognition under Consideration of the Emotion Component Process Model](https://mlsanity.com/paper/2107.12895/) (2021)
- [Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled](https://mlsanity.com/paper/2103.01667/) (2021)
- [x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations](https://mlsanity.com/paper/2203.10909/) (2022)
- [APPReddit: a Corpus of Reddit Posts Annotated for Appraisal](https://mlsanity.com/paper/2205.15627/) (2022)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts](https://mlsanity.com/paper/2402.03223/) (2024)
- [Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations](https://mlsanity.com/paper/2102.12858/) (2021)
- [Emotion Stimulus Detection in German News Headlines](https://mlsanity.com/paper/2107.12920/) (2021)
- [Experiencer-Specific Emotion and Appraisal Prediction](https://mlsanity.com/paper/2210.12078/) (2022)
- [Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions](https://mlsanity.com/paper/2307.14004/) (2023)
- [LEIA: Linguistic Embeddings for the Identification of Affect](https://mlsanity.com/paper/2304.10973/) (2023)
- [Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedging](https://mlsanity.com/paper/2507.17409/) (2025)
- [Guilt Detection in Text: A Step Towards Understanding Complex Emotions](https://mlsanity.com/paper/2303.03510/) (2023)
- [Automatic Emotion Experiencer Recognition](https://mlsanity.com/paper/2305.16731/) (2023)
- [Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions](https://mlsanity.com/paper/2603.22973/) (2026)
- [Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders](https://mlsanity.com/paper/2604.25866/) (2026)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)
- [Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts](https://mlsanity.com/paper/2505.24427/) (2025)
- [Automatic Classifiers Underdetect Emotions Expressed by Men](https://mlsanity.com/paper/2601.04730/) (2026)
- [Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding](https://mlsanity.com/paper/2604.00819/) (2026)
- [mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection](https://mlsanity.com/paper/2605.02695/) (2026)

## Abstract (arXiv)

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.