I’m working on a system that maps emotion vectors into Jungian-style archetypes, and I’d love suggestions on the best methodology/framework for doing this.
Current setup:
- I have ~28 normalized emotions: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, neutral
Goal:
Map combinations/intensities of these emotions into archetypes such as:
https://conorneill.com/2018/04/21/understanding-personality-the-12-jungian-archetypes/
- Explorer
- Sage
- Hero
- Lover
- Creator
- Outlaw
- Caregiver
- Jester etc.
Right now I’m experimenting with:
emotion → motivational drives → archetypes
Example:
- curiosity → freedom / understanding
- fear → safety / control
- pride → mastery / power
Questions:
- Is there any established psychological framework for mapping emotions to archetypes/motivations?
- Would you approach this using:
- heuristic weighting,
- embeddings,
- clustering,
- latent dimensions,
- or supervised learning?
- Are Jungian archetypes even a good structure for this, or should I instead derive emergent archetypes from clustering emotional space?
- Any papers/books/research areas worth looking into?
- Any suggestions for making the mappings feel psychologically coherent instead of arbitrary?
I’m especially interested in:
- affective computing
- computational psychology
- personality systems
- semantic embeddings
- narrative archetypes
- latent trait modeling
u/One_Reference612 — 9 days ago