# Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance

arXiv: https://arxiv.org/abs/2110.06847 · HTML: https://mlsanity.com/paper/2110.06847/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [Dimensional Emotion Detection from Categorical Emotion](https://mlsanity.com/paper/1911.02499/) (2019)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts](https://mlsanity.com/paper/2008.02250/) (2020)
- [Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution](https://mlsanity.com/paper/1501.00960/) (2015)
- [Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter](https://mlsanity.com/paper/2007.12988/) (2020)
- [Zipf's law holds for phrases, not words](https://mlsanity.com/paper/1406.5181/) (2014)
- [Mining Valence, Arousal, and Dominance - Possibilities for Detecting Burnout and Productivity?](https://mlsanity.com/paper/1603.04287/) (2016)
- [Measuring the happiness of large-scale written expression: Songs, Blogs, and Presidents](https://mlsanity.com/paper/1703.09774/) (2017)
- [How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter](https://mlsanity.com/paper/2003.12614/) (2020)
- [Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy](https://mlsanity.com/paper/2008.07301/) (2020)

## Abstract (arXiv)

From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arousal, and dominance (VAD). These essential dimensions have become the cornerstone of sentiment analysis across many fields. By re-examining first types and then tokens for the English language, and through the use of automatically annotated histograms -- `ousiograms' -- we find here that: The essence of meaning conveyed by words is instead best described by a goodness-power-aggression-danger-structure circumplex framework (GPADS); that large-scale English language corpora reveal a systematic bias toward safe, low-danger words; and that the power-danger-structure (PDS) framework is the minimal framework that represents essential meaning. We find remarkable congruences between the GPADS framework and other spaces including mental states and fictional archetypes, and we construct and demonstrate a prototype ousiometer.