# NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms

arXiv: https://arxiv.org/abs/2503.23547 · HTML: https://mlsanity.com/paper/2503.23547/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News](https://mlsanity.com/paper/1405.1605/) (2014)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)

## Built on by (most cited first, 27 of 27)

- [Valence-Arousal Subspace in LLMs: Circular Emotion Geometry and Multi-Behavioral Control](https://mlsanity.com/paper/2604.03147/) (2026)
- [Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions](https://mlsanity.com/paper/2508.20764/) (2025)
- [Words of Warmth: Trust and Sociability Norms for over 26k English Words](https://mlsanity.com/paper/2506.03993/) (2025)
- [EmoCaliber: Advancing Reliable Visual Emotion Comprehension via Confidence Verbalization and Calibration](https://mlsanity.com/paper/2512.15528/) (2025)
- [KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models](https://mlsanity.com/paper/2507.19962/) (2025)
- [Affective Context Amplifies Sycophancy in LLM Responses](https://mlsanity.com/paper/2608.21242/) (2026)
- [Computational Multi-Agents Society Experiments: Social Modeling Framework Based on Generative Agents](https://mlsanity.com/paper/2508.17366/) (2025)
- [Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples](https://mlsanity.com/paper/2603.02370/) (2026)
- [Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable](https://mlsanity.com/paper/2609.37616/) (2026)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study](https://mlsanity.com/paper/2604.00005/) (2026)
- [MooD: Perception-Enhanced Efficient Affective Image Editing via Continuous Valence-Arousal Modeling](https://mlsanity.com/paper/2605.02521/) (2026)
- [Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation](https://mlsanity.com/paper/2507.21120/) (2025)
- [TOPol: Capturing and Explaining Multidimensional Semantic Polarity Fields and Vectors](https://mlsanity.com/paper/2510.25069/) (2025)
- [Quantifying Emotional Tone in Tolkien's The Hobbit: Dialogue Sentiment Analysis with RegEx, NRC-VAD, and Python](https://mlsanity.com/paper/2512.10865/) (2025)
- [Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence](https://mlsanity.com/paper/2601.06316/) (2026)
- [Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features](https://mlsanity.com/paper/2602.22846/) (2026)
- [Quantifying Media Representation Dynamics Across 25 Years of News Reporting on Policing-related Deaths](https://mlsanity.com/paper/2606.06812/) (2026)
- [End-to-End Machine Learning for Depressive State Classification via EEG and fNIRS](https://mlsanity.com/paper/2606.11555/) (2026)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)
- [LLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning](https://mlsanity.com/paper/2606.14784/) (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)
- [Replicating the Geometry of Emotion Representations in a Base Open-Weights Model](https://mlsanity.com/paper/2609.22208/) (2026)
- [PersMem: Internalizing Personality into Dual-Pathway Memory for LLM Agents](https://mlsanity.com/paper/2609.34372/) (2026)
- [Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generation](https://mlsanity.com/paper/2609.34419/) (2026)
- [MASCRDM: Multi-Agent System for Compliance Risk Detection and Mitigation in Training Process of Large Language Models](https://mlsanity.com/paper/2609.39107/) (2026)

## Abstract (arXiv)

Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D) (also referred to in social cognition research as Competence (C)). These dimensions impact various aspects of our lives from social competence and emotion regulation to success in the work place and how we view the world. We present here the NRC VAD Lexicon v2, which has human ratings of valence, arousal, and dominance for more than 55,000 English words and phrases. Notably, it adds entries for ~25k additional words to v1.0. It also now includes for the first time entries for common multi-word phrases (~10k). We show that the associations are highly reliable. The lexicon enables a wide variety of research in psychology, NLP, public health, digital humanities, and social sciences. The NRC VAD Lexicon v2 is made freely available for research through our project webpage.