# Appraisal Theories for Emotion Classification in Text

arXiv: https://arxiv.org/abs/2003.14155 · HTML: https://mlsanity.com/paper/2003.14155/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [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)
- [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)
- [Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models](https://mlsanity.com/paper/2310.14389/) (2023)
- [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)
- [Emotion-Aware, Emotion-Agnostic, or Automatic: Corpus Creation Strategies to Obtain Cognitive Event Appraisal Annotations](https://mlsanity.com/paper/2102.12858/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (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)
- [Improving Language Models for Emotion Analysis: Insights from Cognitive Science](https://mlsanity.com/paper/2406.10265/) (2024)
- [Modeling Subjectivity in Cognitive Appraisal with Language Models](https://mlsanity.com/paper/2503.11381/) (2025)
- [Exploratory Study into Relations between Cognitive Distortions and Emotional Appraisals](https://mlsanity.com/paper/2503.15979/) (2025)
- [Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework](https://mlsanity.com/paper/2509.17844/) (2025)
- [On the Complementarity of Images and Text for the Expression of Emotions in Social Media](https://mlsanity.com/paper/2202.07427/) (2022)
- [Automatic Emotion Experiencer Recognition](https://mlsanity.com/paper/2305.16731/) (2023)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [Topic Bias in Emotion Classification](https://mlsanity.com/paper/2312.09043/) (2023)
- [Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings](https://mlsanity.com/paper/2503.16883/) (2025)
- [Categorical Emotions or Appraisals - Which Emotion Model Explains Argument Convincingness Better?](https://mlsanity.com/paper/2511.07162/) (2025)
- [mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection](https://mlsanity.com/paper/2605.02695/) (2026)
- [Emotional Labor Strategy Preferences in LLM Personas](https://mlsanity.com/paper/2609.00310/) (2026)
- [Some Emotions Run Deeper: Layer-wise Probing and Causal Intervention in Large Language Models](https://mlsanity.com/paper/2609.01279/) (2026)
- [Mixed Feelings, Single Polarity](https://mlsanity.com/paper/2610.15977/) (2026)

## Abstract (arXiv)

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words "I am afraid."). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.