# Emotion Recognition under Consideration of the Emotion Component Process Model

arXiv: https://arxiv.org/abs/2107.12895 · HTML: https://mlsanity.com/paper/2107.12895/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [Appraisal Theories for Emotion Classification in Text](https://mlsanity.com/paper/2003.14155/) (2020)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [An Analysis of Emotion Communication Channels in Fan Fiction: Towards Emotional Storytelling](https://mlsanity.com/paper/1906.02402/) (2019)

## Built on by (most cited first, 12 of 12)

- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs](https://mlsanity.com/paper/2412.15993/) (2024)
- [x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations](https://mlsanity.com/paper/2203.10909/) (2022)
- [Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation](https://mlsanity.com/paper/2305.10446/) (2023)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [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)
- [Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework](https://mlsanity.com/paper/2509.17844/) (2025)
- [Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models](https://mlsanity.com/paper/2509.19595/) (2025)
- [On the Complementarity of Images and Text for the Expression of Emotions in Social Media](https://mlsanity.com/paper/2202.07427/) (2022)
- [Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis](https://mlsanity.com/paper/2405.14385/) (2024)

## Abstract (arXiv)

Emotion classification in text is typically performed with neural network models which learn to associate linguistic units with emotions. While this often leads to good predictive performance, it does only help to a limited degree to understand how emotions are communicated in various domains. The emotion component process model (CPM) by Scherer (2005) is an interesting approach to explain emotion communication. It states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency. We hypothesize that these components are associated with linguistic realizations: an emotion can be expressed by describing a physiological bodily reaction ("he was trembling"), or the expression ("she smiled"), etc. We annotate existing literature and Twitter emotion corpora with emotion component classes and find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader. We further include the CPM in a multitask learning model and find that this supports the emotion categorization. The annotated corpora are available at https://www.ims.uni-stuttgart.de/data/emotion.