Valence-Arousal-Dominance Framework

• Valence-Arousal-Dominance is a three-dimensional model capturing evaluative, activation, and control aspects of emotion, derived from robust factor analytic methods.
• The framework supports advanced computational models by integrating neural architectures, fuzzy logic, and clustering techniques to enhance emotion recognition.
• VAD is widely applied in NLP, HCI, and mental health monitoring, utilizing large-scale lexica and empirical validations for cross-domain affective analysis.

The Valence–Arousal–Dominance (VAD) framework is a three-dimensional, continuous representation of affective states that enables fine-grained quantification of emotions across diverse modalities, domains, and computational settings. Established through foundational factor analysis of human affective ratings, VAD decomposes emotional phenomena into orthogonal axes: valence (pleasure–displeasure), arousal (activation–deactivation), and dominance (power–submission). This space underpins state-of-the-art approaches in affective computing, computational linguistics, human-computer interaction, and behavioral analysis.

1. Theoretical Foundations and Factor Analytic Origins

The VAD framework arose from extensive psychometric research on emotional meaning. Words, sentences, and stimuli rated by large participant panels on bipolar adjective scales were consistently explained by three dominant factors: evaluation/valence, activity/arousal, and potency/dominance. Mathematically, ratings vector $x$ for an item can be expressed as $x = \Lambda F + \varepsilon$, where $F$ is the latent VAD factor vector, $\Lambda$ the loading matrix, and $\varepsilon$ noise. Early studies (Osgood, Russell, Mehrabian) demonstrated that the first three eigenvalues of the inter-item correlation matrix capture the majority of variance, with remaining factors negligible (Mohammad, 30 Mar 2025, Mohammad, 25 Nov 2025). The axes are conceptually and statistically near-orthogonal (typical $|\rho(V,A)| < 0.2$, $|\rho(V,D)| \approx 0.3$, $|\rho(A,D)| \approx 0.1$), supporting independent modeling (Mohammad, 30 Mar 2025).

• Valence (V): Evaluative “pleasantness.” High values indicate positivity (e.g., "celebration"), low values, negativity (e.g., "funeral").
• Arousal (A): Activation–deactivation spectrum, differentiating excited states from calmness (e.g., "adrenaline" vs. "relax").
• Dominance (D): Degree of control, capturing power, agency, or submission (e.g., "command" vs. "yield").

These axes are operationalized on bounded intervals—typically $[-1,1]$, $[0,1]$, or $[1,9]$, with scale anchoring via explicit instructions to raters.

2. Lexicon Construction, Annotation, and Reliability

Large-scale VAD lexica are foundational resources for empirical studies. The NRC VAD Lexicon v2 (Mohammad, 30 Mar 2025) provides 55,001 English terms (44,928 unigrams and 10,073 multi-word expressions), annotated via crowd-sourced judgments. Each entry receives nine independent ratings on $7$-point bipolar scales for V, A, and D, rescaled to $[-1, 1]$. Reliability is quantified using split-half reliability statistics (SHR), Cronbach’s alpha, and ICC:

Dimension	Mean #Annotators	SHR (ρ)	SHR (r)
Valence	7.83	0.98	0.99
Arousal	7.96	0.97	0.98
Dominance	8.06	0.96	0.96

These values indicate high inter-rater consistency (Mohammad, 30 Mar 2025, Mohammad, 25 Nov 2025). MWEs display strong but partially non-compositional VAD characteristics: for valence, bigram means correlate $r \approx 0.85$ with constituent averages, but up to $1.7\%$ of "neutral-neutral" MWEs are strongly valenced idioms (e.g., "over the moon") (Mohammad, 25 Nov 2025).

3. Computational Modeling: Neural Architectures and Fuzzy Extensions

The VAD space serves as a backbone for diverse deep learning models:

Fuzzy VAD Representations:

To address subjectivity and uncertainty in self-reports, interval type-2 fuzzy sets for each VAD axis partition the $[1,9]$ scale into low, medium, and high, using Gaussian-shaped upper (UMF) and lower (LMF) membership functions. The “footprint of uncertainty” (FoU) between UMF and LMF formalizes representational ambiguity (Asif et al., 15 Jan 2024). Deep architectures integrate spatial (CNN), temporal (LSTM), and VAD-fuzzy branches. Empirically, emotion recognition on DENS EEG achieves:

Model Variant	Accuracy (%)
No VAD	93.54
Crisp VAD	95.01
Type-2 Fuzzy VAD (best)	96.09
Cuboid fuzzy lattice	95.75

Ablations show VAD input always improves over pure EEG, with type-2 fuzzy outperforming crisp and type-1 baselines. Cross-subject generalization is enhanced ($+4-5\%$ in hardest conditions) (Asif et al., 15 Jan 2024).

Multimodal and Multitask Deep Models:

Recent works fuse text, speech, and visual inputs into latent VAD representations. For instance, dual-tower frameworks pretrain independent uncertainty-aware VAD predictors per modality, then identify and resolve cross-modal inconsistencies via gated transformers (Li et al., 24 Sep 2025). Variational disentanglement architectures force separate latent dimensions for valence, arousal, and dominance, grounded using external lexicon scores and auxiliary regression/classification objectives (Xu et al., 26 Feb 2025).

Continuous-to-Discrete Bridging:

K-means clustering in the 3D VAD space allows mapping continuous predictions back to discrete labels for evaluation/compatibility (Jia et al., 12 Sep 2024). Proxy-based human-centric protocols use geometric animation proxies for mapping emotion words to VAD locations, mitigating mapping biases and supporting multimodal dataset harmonization (Wrobel, 16 Nov 2025).

4. Applications Across Domains

Natural Language Processing:

VAD is leveraged to extract affective features, produce sentiment-specific word embeddings, and drive emotion intensity modeling (Mohammad, 30 Mar 2025). Dimensional regression models permit zero-shot mapping from categorical to fine-grained VAD via lexicon or neural-based procedures, with Earth Mover’s Distance providing a natural loss for ordinal regression (Park et al., 2019). Application areas include stance detection, where inclusion of disentangled VAD latent variables yields 15.5 F₁ gains over BERT baselines in political discourse analysis (Xu et al., 26 Feb 2025).

Affective Computing and HCI:

Continuous VAD-based control in TTS enables parametric, smooth, and expressive generation of emotional speech, obviating the need for a fixed emotional label inventory (Rabiee et al., 2019). In closed-loop human-robot interaction, real-time EEG-driven PAD estimation modulates operator support strategies (e.g., tailored audio stimulus) for maintaining optimal cognitive-affective states and improving behavioral performance (Alfatlawi, 2021).

Productivity and Mental Health Monitoring:

Textual VAD mining in large-scale software repositories enables detection of burnout signatures and productivity bottlenecks: higher issue priority raises arousal, extended discussion lowers valence, and resolution completion increases both valence and dominance. Extension to predictive models shows VAD metrics alone offer statistically significant performance boosts over sentiment/politeness features for time-to-resolution classification (Mäntylä et al., 2016).

Digital Humanities, Public Health, and Social Sciences:

VAD scores allow quantification of the emotional arc in literature, tracking collective mood in social media, and modeling stereotype content via dominance and valence axes (Mohammad, 30 Mar 2025, Mohammad, 25 Nov 2025).

5. Methodological Innovations and Evaluation

Fuzzy and Uncertainty Modeling:

Type-2 fuzzy membership formalizes human reporting ambiguity, capturing overlap and gradience in affective experience (Asif et al., 15 Jan 2024). In deep models, uncertainty is explicitly represented by outputting mean and variance per VAD dimension, allowing robust fusion and consistency checks across modalities (Li et al., 24 Sep 2025).

Disentanglement and Semantic Grounding:

Variational methods (e.g., VAEs) explicitly separate VAD (affect) from content, using multi-task objectives with external lexicon alignment to enforce semantic interpretability. Empirical studies confirm that such disentanglement is essential: ablations removing VAD structure degrade F₁ and reconstruction quality sharply (Xu et al., 26 Feb 2025).

Continuous–Discrete Conversion:

Proxy-based human protocols (geometric animation encoding, self-assessment) and clustering/generative strategies underpin robust mapping between standard emotion categories and continuous VAD coordinates. These bridges are empirically validated through replication studies and downstream performance analyses (Wrobel, 16 Nov 2025, Jia et al., 12 Sep 2024).

6. Limitations and Prospective Directions

Domain and Lexicon Coverage:

Current VAD lexica may face domain adaptation challenges: domain-specific jargon and compositional semantics in technical or non-English corpora may be misrepresented. For multi-word expressions, compositionality is substantial but not complete; idioms and fixed expressions often bear non-compositional affective meaning (Mohammad, 25 Nov 2025).

Demographic and Cultural Variation:

Most annotation studies to date have relied on Western, English-speaking populations. Expressive nuance and VAD centroids for emotion words may shift cross-culturally, suggesting a need for larger, multilingual calibration efforts (Wrobel, 16 Nov 2025).

Temporal Dynamics and Interactivity:

Static VAD per utterance is the norm; research on within-utterance or dialogue-level dynamics, as well as real-time feedback systems for emotion regulation, is emergent and highlights promising directions in both affective computing and assistive technology (Alfatlawi, 2021, Rabiee et al., 2019).

---

In sum, the Valence–Arousal–Dominance framework provides a mathematically grounded, empirically validated coordinate system for modeling affect, supporting a breadth of methodological and applied advancements, and laying a foundation for continued cross-disciplinary exploration in the affective sciences and machine learning (Mohammad, 30 Mar 2025, Asif et al., 15 Jan 2024, Xu et al., 26 Feb 2025, Mohammad, 25 Nov 2025).