# Dimensional Emotion Detection from Categorical Emotion

arXiv: https://arxiv.org/abs/1911.02499 · HTML: https://mlsanity.com/paper/1911.02499/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 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)
- [Cluster-Level Contrastive Learning for Emotion Recognition in Conversations](https://mlsanity.com/paper/2302.03508/) (2023)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series](https://mlsanity.com/paper/2301.09279/) (2023)
- [Disentangled Variational Autoencoder for Emotion Recognition in Conversations](https://mlsanity.com/paper/2305.14071/) (2023)
- [Modeling Emotional Trajectories in Written Stories Utilizing Transformers and Weakly-Supervised Learning](https://mlsanity.com/paper/2406.02251/) (2024)
- [Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing](https://mlsanity.com/paper/2409.19025/) (2024)
- [Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification](https://mlsanity.com/paper/2504.16856/) (2025)
- [Personality-affected Emotion Generation in Dialog Systems](https://mlsanity.com/paper/2404.07229/) (2024)
- [Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models](https://mlsanity.com/paper/2408.04420/) (2024)
- [Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus](https://mlsanity.com/paper/2210.06405/) (2022)
- [UDDETTS: Unifying Discrete and Dimensional Emotions for Controllable Emotional Text-to-Speech](https://mlsanity.com/paper/2505.10599/) (2025)
- [Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features](https://mlsanity.com/paper/2212.09465/) (2022)
- [Automatic Emotion Modelling in Written Stories](https://mlsanity.com/paper/2212.11382/) (2022)
- [Unveiling Affective Polarization Trends in Parliamentary Proceedings](https://mlsanity.com/paper/2512.05231/) (2025)
- [Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance](https://mlsanity.com/paper/2110.06847/) (2021)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [Emotion Detection with Transformers: A Comparative Study](https://mlsanity.com/paper/2403.15454/) (2024)
- [Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification](https://mlsanity.com/paper/2404.01805/) (2024)
- [Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis](https://mlsanity.com/paper/2605.26262/) (2026)
- [A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories](https://mlsanity.com/paper/2606.29068/) (2026)
- [EmoLASP: Emotion Recognition with Language Models and Answer Set Programming](https://mlsanity.com/paper/2608.29035/) (2026)
- [Affect-Prototype Guided Fusion for Open-Vocabulary Incomplete Multi-modal Emotion Recognition](https://mlsanity.com/paper/2609.16962/) (2026)
- [Mixed Feelings, Single Polarity](https://mlsanity.com/paper/2610.15977/) (2026)

## Abstract (arXiv)

We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover's Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.