mlsanityData to 7 Oct 2026

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

arXiv 1905.13618 ↗First posted 31 May 2019cs.CL cs.AI cs.HCcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 6; 2024: 2; 2025: 2; 2026: 5.

25 papers.

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  1. 2022Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Predictioncited by 45
  2. 2024Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directionscited by 26
  3. 2022Few-Shot Learning with Siamese Networks and Label Tuningcited by 15
  4. 2022Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corporacited by 13
  5. 2021Emotion Recognition under Consideration of the Emotion Component Process Modelcited by 12
  6. 2021Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangledcited by 10
  7. 2022x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotationscited by 8
  8. 2022APPReddit: a Corpus of Reddit Posts Annotated for Appraisalcited by 7
  9. 2023Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approachescited by 7
  10. 2024English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Promptscited by 7
  11. 2021Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotationscited by 6
  12. 2021Emotion Stimulus Detection in German News Headlinescited by 4
13 more
  1. 2022Experiencer-Specific Emotion and Appraisal Predictioncited by 4
  2. 2023Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditionscited by 4
  3. 2023LEIA: Linguistic Embeddings for the Identification of Affectcited by 3
  4. 2025Investigating Subjective Factors of Argument Strength: Storytelling, Emotions, and Hedgingcited by 2
  5. 2023Guilt Detection in Text: A Step Towards Understanding Complex Emotionscited by 1
  6. 2023Automatic Emotion Experiencer Recognitioncited by 1
  7. 2026Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisionscited by 1
  8. 2023Topic Bias in Emotion Classificationcited by 0
  9. 2025Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Postscited by 0
  10. 2026Automatic Classifiers Underdetect Emotions Expressed by Mencited by 0
  11. 2026Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understandingcited by 0
  12. 2026Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoderscited by 0
  13. 2026mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detectioncited by 0