Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance
arXiv:2110.06847v3 Announce Type: replace Abstract: 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
The proliferation of AI and sentiment analysis models necessitates a more nuanced and accurate understanding of meaning, challenging long-held psychological frameworks.
A revised framework for understanding the essence of meaning could fundamentally alter the accuracy and capabilities of AI systems dealing with language, sentiment, and emotional intelligence.
The foundational understanding of how meaning is processed and quantified shifts from Valence-Arousal-Dominance to a Power-Danger-Structure framework, potentially reshaping AI model development.
- · AI/ML researchers
- · NLP developers
- · Sentiment analysis companies
- · Cognitive science
- · Traditional sentiment analysis methodologies
- · Psycholinguistics (if resistant to change)
Refined semantic understanding in AI models leads to more accurate and reliable linguistic processing.
New AI applications emerge that can better interpret and generate human-like emotional and contextual communication.
The development of truly 'understanding' AI agents could accelerate, profoundly impacting human-computer interaction and automation.
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Read at arXiv cs.CL