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Emotionics: A Framework for Modeling Human Emotions as Elements for AI Understanding

Emotionics: A Framework for Modeling Human Emotions as Elements for AI Understanding


1. Background and Motivation

Human–AI interaction is rapidly becoming a central challenge in the 21st century. While AI excels at processing language and numbers, it still lacks a robust framework to understand human emotions. Existing sentiment analysis often reduces emotions to “positive/negative,” which is insufficient for nuanced communication, trust-building, and decision-making.

Emotionics is a conceptual framework that treats emotions as elements in a periodic table of emotions, inspired by both chemistry and cognitive science. Just as chemistry models invisible atoms to understand matter, Emotionics models emotions to understand invisible human states.





2. Core Concepts of Emotionics

  • Periodic Table of Emotions (Ten):
    A structured map of fundamental emotions, each treated as an “element” with defined relationships.
  • Zetsu (Self-Mapping):
    Mapping one’s own internal emotional state to the periodic table.
  • Gyo (Other-Mapping):
    Inferring another person’s emotions from speech, behavior, or expression.
  • Hatsu (Induction):
    Influencing or guiding another’s emotional state intentionally.
  • VAD Mapping (Valence–Arousal–Dominance):
    Each emotion element can be numerically represented in a three-dimensional coordinate system (positive/negative, arousal, dominance).

https://github.com/Kouhei-Takagi/Project-SAYA/tree/main/assets


3. Research Objectives

This proposal invites collaboration to model and implement Emotionics computationally, with the following objectives:

  1. Formalize the periodic table of emotions into a machine-readable dataset (JSON, VAD values, seed vocabularies).
  2. Develop algorithms for Gyo (emotion extraction) from text and multimodal signals.
  3. Explore methods for Hatsu (emotion induction) in human–AI interaction while ensuring ethical boundaries.
  4. Evaluate the framework across different cultures, languages, and contexts to test universality and adaptability.


4. Potential Applications

  • Human–AI Communication: More natural and empathetic conversational agents.
  • Education and Coaching: Tools that adapt to learner emotions.
  • Healthcare and Mental Health: Early detection of emotional distress, supportive interventions.
  • Social Simulations: Modeling collective emotions in economics, politics, and culture.


5. Why Collaborate on Emotionics?

  • Novelty: Emotionics offers a new paradigm that goes beyond sentiment polarity.
  • Structure: The periodic table and Ten→Gyo→Hatsu pipeline provide a clear roadmap for implementation.
  • Societal Impact: A robust model of emotions can benefit not only AI research but also education, healthcare, and governance.


6. Next Steps

We invite researchers, laboratories, and companies to:

  • Collaborate on data collection and model building.
  • Test Emotionics in experimental AI systems.
  • Contribute to the open development of emotion modeling standards.

If you are interested in information beyond what is published on the blog, please feel free to reach out using the Contact Form on the right.


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