# EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis

arXiv: https://arxiv.org/abs/2205.01996 · HTML: https://mlsanity.com/paper/2205.01996/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 40 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 1 of 1)

- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)

## Built on by (most cited first, 30 of 76)

- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2502.12378/) (2025)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://mlsanity.com/paper/2304.00815/) (2023)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs](https://mlsanity.com/paper/2408.17026/) (2024)
- [Decoding Emotion in the Deep: A Systematic Study of How LLMs Represent, Retain, and Express Emotion](https://mlsanity.com/paper/2510.04064/) (2025)
- [Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers](https://mlsanity.com/paper/2302.14021/) (2023)
- [APPReddit: a Corpus of Reddit Posts Annotated for Appraisal](https://mlsanity.com/paper/2205.15627/) (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)
- [SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks](https://mlsanity.com/paper/2402.01980/) (2024)
- [Text-Transport: Toward Learning Causal Effects of Natural Language](https://mlsanity.com/paper/2310.20697/) (2023)
- [Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions](https://mlsanity.com/paper/2205.11867/) (2022)
- [Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations](https://mlsanity.com/paper/2308.16349/) (2023)
- [Improving Language Models for Emotion Analysis: Insights from Cognitive Science](https://mlsanity.com/paper/2406.10265/) (2024)
- [iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News](https://mlsanity.com/paper/2503.03335/) (2025)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human-LLM Judgment Gaps](https://mlsanity.com/paper/2604.27345/) (2026)
- [Ethos and Pathos in Online Group Discussions: Corpora for Polarisation Issues in Social Media](https://mlsanity.com/paper/2404.04889/) (2024)
- [Personality-affected Emotion Generation in Dialog Systems](https://mlsanity.com/paper/2404.07229/) (2024)
- [Modelling Emotion Dynamics in Song Lyrics with State Space Models](https://mlsanity.com/paper/2210.09434/) (2022)
- [Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer](https://mlsanity.com/paper/2305.15582/) (2023)
- [Representation Learning with Conditional Information Flow Maximization](https://mlsanity.com/paper/2406.05510/) (2024)
- [Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting](https://mlsanity.com/paper/2409.12524/) (2024)
- [Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English](https://mlsanity.com/paper/2504.17974/) (2025)
- [Fluent but Unfeeling: The Emotional Blind Spots of Language Models](https://mlsanity.com/paper/2509.09593/) (2025)
- [Uncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP](https://mlsanity.com/paper/2605.24773/) (2026)

## Abstract (arXiv)

We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer's and reader's emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader's perspective in terms of IAA and rating intensity, and achieve close-to-human performance when mapping between dimensional and categorical formats.