Meaning Alignment Index

War and Peace. Part 1a

Valence–Arousal–Dominance (VAD) for Text-Based Prosodic Temperature

This post continues the work of MAI — showing how measurable meaning can illuminate emotional currents inside geopolitical communication, especially when translation hides critical nuance.

Months ago, when conducting emoji tests with LLMs, we introduced the Valence–Arousal–Dominance (VAD) model. The reason we leveraged VAD was that we needed to extract emotions (if any) from emojis.

VAD provides a compact three-dimensional geometry of emotional tone that is interpretable across languages and cultures:

· Valence – how positive or negative the emotion is.

· Arousal – the level of activation or intensity.

· Dominance – the degree of control or power expressed in the text

Conceptual VAD geometry connected to Prosodic Temperature (Pt).
(Note: This is a conceptual visualization; full implementation is future work.)

Now, let’s apply this to a real-world scenario. On November 21, 2025 Vladimir Zelensky gave a speech and he repeated the word “dignity” over and over again

Note: the key phrase in Ukrainian is “Або втрата гідності” and his entire speech in Ukrainian can be found here: https://www.president.gov.ua/news/yednist-potribna-nam-yak-nikoli-abi-v-nashomu-domi-buv-dosto-101493

The English word “dignity” felt strangely soft, given the political stakes. Only after applying MAI + VAD did the real divergence emerge: the translation is technically correct but semantically misleading.

Upon delving into MAI and VAD (calculations below), the divergence of meaning emerged. The issue is: “We often confuse translation with equivalence.”

In English, “dignity” sounds:

· soft

· abstract

· moral-theoretical

In Ukrainian political discourse, “гідності” triggers:

· sovereignty

· survival of identity

· defiance against domination

· national self-respect

The same word has different VAD geometry depending on the culture.

Table: The Emotional Geometry of “Гідність / Dignity”

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MAI: Meaning Risk Intelligence System (MRIS)

Let’s put the pieces together to understand the potential and specifically address Zelensky’s “dignity” speech as an example.

Formulas:

Where:

Ac = Alignment Coherence

He = Entropic Divergence

Pt = Prosodic Temperature

Td = Topological Distance

Where:

D_o = Dominance

V_a = Valence

A_r = Arousal

With these formulas, and the aid of Artificial Intelligence, we can create (1) Mind Maps, (2) Meaning Temperature Maps, (3) VAD Topology Maps, (4) Conceptual Misalignment Graphs, (5) global emotional geometry around a conflict, across:

· news

· social media

· political speeches

· diplomatic messaging

· regional sources

· non-English sources

· propaganda channels

· cultural statements

· activist networks

· diaspora groups

· think-tank analysis

· NGO alerts

(Note: The interface shown here is conceptual as of November 2025. The underlying analytics — VAD, Pt, mind-mapping, conceptual misalignment — already exist and are demonstrably implementable.)

The Meaning Risk Intelligence System produces:

Mind Maps

Mind Maps identify key words. Examples: dignity, violence, hunger, genocide, etc. In Mind Maps, the words are different sizes based on the frequency of use. And with a Meaning Risk Intelligence System, we can drill down on each word to acquire further detail.

Prosodic-Temperature Gradients

How the emotional force is shifting. Are people screaming, shouting, crying?

VAD Topology Maps

A measure of the geography of emotional pressure.

Conceptual Misalignment Graphs

Where one word like “pause” or “dignity” means radically different things in different contexts. The Meaning Risk Intelligence System is used for:

· peacebuilding

· atrocity prevention

· diplomacy

· conflict monitoring

The measurements are:

· perceived humiliation

· moral threat

· identity injury

· loss of dignity

· asymmetry of sacrifice

· grievance accumulation

· escalation rhetoric

· victimhood narratives

· retaliatory framing

dehumanization signals

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Або втрата гідності

Applying P_t–VAD + mind-map analysis to:

· Zelensky

· U.S. statements

· EU speeches

· Russian speeches

· Ukrainian social media

We would see incompatible emotional geometries.

While this would not in itself resolve the war —it will:

· reduce miscommunication

· expose moral gaps

· highlight emotional triggers

· identify high-risk rhetoric

· help negotiators avoid inflammatory words

· create shared conceptual ground

Zelenski’s speech creates a high-temperature, high-dominance cluster around “гідності” (dignity). But in English translations, that nuance is lost.

Analyzing Zelinsky’s entire speech:

Pt = 0.74 reflects:

· Urgency

· Emotional activation

· High-stakes framing

· Repeated reference to dignity, threat, unity, survival

· Appeal to collective identity

· Mobilization tone (24/7, “боротися”, “не зраджу”, “не дамо”)

The speech contains: threats, danger, suffering, war, death, loss, “найважчих моментів”, “удари”, “втрата”, “стримуємо”, “ризики”

In MAI terms, this creates a high-temperature emotional field, signaling identity threat, grievance accumulation, and existential framing — all crucial for peace analysts.

###Valence = 0.18 (low).

The dominant verbs are: “боротися”, “не зраджу”, “треба зібратись”, “припинити”, “стільки, скільки це буде потрібно”

Dominance =0.62

Word usage: “я буду боротися”, “ми не зрадили”, “ми не дамо” – are repeated emphasis on unity, sovereignty.

VAD of “гідності” across speech frequency:

· Valence: +0.22 (slightly positive but solemn; moral gravity, not joy)

· Arousal: +0.71 (very high)

· Dominance: +0.78 (extremely high)

Meaning: “Dignity” in Ukrainian rhetoric carries:

· moral authority

· identity preservation

· national selfhood

· anti-colonial resistance

· agency

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Meaning is not universal. It is shaped by history, trauma, culture, and survival. Ceasefire has different meanings. Dignity has different meanings. MAI is an attempt to measure that geometry and guide us to greater understanding and empathy.

Thank you again for walking with me on this journey — exploring how meaning can be measured — and how doing so may help humans and AI understand each other not just through language, but through shared emotional geometry.

Russ Palmer
Independent Researcher, AMS and MAI Projects

Exploring how meaning emerges without a mind—and why that matters now.
🔗 Google Scholar Profile

Zenodo: https://zenodo.org/records/16643857

P.S. The VAD calculations are prototype results generated using multilingual embeddings and a small human-verified lexicon. Full methodology will be released in the MAI Working Paper.