koriweb
d8a80f6272
이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해 끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은 과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업. 도구: Datacollect/scripts/link_reconcile_apply.mjs Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
211 lines
7.1 KiB
Markdown
211 lines
7.1 KiB
Markdown
---
|
||
id: wiki-2026-0508-plutchiks-wheel-of-emotions
|
||
title: Plutchik's Wheel of Emotions
|
||
category: 10_Wiki/Topics
|
||
status: verified
|
||
canonical_id: self
|
||
aliases: [plutchik-wheel, emotion-wheel, primary-emotions]
|
||
duplicate_of: none
|
||
source_trust_level: A
|
||
confidence_score: 0.9
|
||
verification_status: applied
|
||
tags: [emotion, psychology, affective-computing, nlp]
|
||
raw_sources: []
|
||
last_reinforced: 2026-05-10
|
||
github_commit: pending
|
||
tech_stack:
|
||
language: Python
|
||
framework: transformers
|
||
---
|
||
|
||
# Plutchik's Wheel of Emotions
|
||
|
||
## 매 한 줄
|
||
> **"매 8 primary emotions × 3 intensities + dyads = 매 emotion taxonomy"**. 매 Robert Plutchik (1980) 매 evolutionary-grounded model 의 propose. 매 8 primary: joy, trust, fear, surprise, sadness, disgust, anger, anticipation. 매 affective computing + emotion-aware NLP 의 widely used.
|
||
|
||
## 매 핵심
|
||
|
||
### 매 8 primary emotions (4 axes)
|
||
1. **매 joy ↔ sadness**
|
||
2. **매 trust ↔ disgust**
|
||
3. **매 fear ↔ anger**
|
||
4. **매 surprise ↔ anticipation**
|
||
|
||
### 매 intensity (radial)
|
||
- 매 joy: ecstasy (high) → joy (mid) → serenity (low).
|
||
- 매 anger: rage → anger → annoyance.
|
||
- 매 fear: terror → fear → apprehension.
|
||
- (매 8 primary × 3 levels = 24 emotions)
|
||
|
||
### 매 dyads (combinations)
|
||
- **매 primary dyads** (adjacent): joy + trust = love, trust + fear = submission, fear + surprise = awe.
|
||
- **매 secondary dyads** (one apart): joy + fear = guilt, anger + joy = pride.
|
||
- **매 tertiary dyads** (two apart): joy + surprise = delight, anger + surprise = outrage.
|
||
- **매 opposite dyads** (across): rare/conflicting (e.g. joy + sadness = bittersweet).
|
||
|
||
### 매 vs other models
|
||
- **매 Ekman (6 basic)**: anger, disgust, fear, happiness, sadness, surprise — 매 universal facial expression-based.
|
||
- **매 GoEmotions (27, Google 2020)**: 매 Reddit-derived, 매 fine-grained.
|
||
- **매 dimensional (VAD)**: valence-arousal-dominance — 매 continuous space.
|
||
- **매 Plutchik 매 advantages**: 매 structured (axes + intensity + combinations), 매 evolutionary grounding.
|
||
|
||
### 매 응용
|
||
1. 매 emotion classification (NLP).
|
||
2. 매 sentiment analysis (richer than pos/neg).
|
||
3. 매 chatbot empathy modeling.
|
||
4. 매 mental health monitoring.
|
||
|
||
## 💻 패턴
|
||
|
||
### Plutchik labels — Python enum
|
||
```python
|
||
from enum import Enum
|
||
|
||
class Plutchik8(Enum):
|
||
JOY = "joy"
|
||
TRUST = "trust"
|
||
FEAR = "fear"
|
||
SURPRISE = "surprise"
|
||
SADNESS = "sadness"
|
||
DISGUST = "disgust"
|
||
ANGER = "anger"
|
||
ANTICIPATION = "anticipation"
|
||
|
||
INTENSITY = {
|
||
"joy": ["serenity", "joy", "ecstasy"],
|
||
"anger": ["annoyance", "anger", "rage"],
|
||
"fear": ["apprehension", "fear", "terror"],
|
||
# ...
|
||
}
|
||
|
||
PRIMARY_DYADS = {
|
||
("joy", "trust"): "love",
|
||
("trust", "fear"): "submission",
|
||
("fear", "surprise"): "awe",
|
||
("surprise", "sadness"): "disapproval",
|
||
("sadness", "disgust"): "remorse",
|
||
("disgust", "anger"): "contempt",
|
||
("anger", "anticipation"): "aggressiveness",
|
||
("anticipation", "joy"): "optimism",
|
||
}
|
||
```
|
||
|
||
### Multi-label emotion classifier (transformers)
|
||
```python
|
||
import torch
|
||
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
||
|
||
# 매 j-hartmann/emotion-english-distilroberta-base — 7 emotions (Plutchik-aligned subset)
|
||
MODEL = "j-hartmann/emotion-english-distilroberta-base"
|
||
tok = AutoTokenizer.from_pretrained(MODEL)
|
||
model = AutoModelForSequenceClassification.from_pretrained(MODEL).eval()
|
||
|
||
def emotion_scores(text):
|
||
inputs = tok(text, return_tensors="pt", truncation=True)
|
||
with torch.no_grad():
|
||
logits = model(**inputs).logits
|
||
probs = logits.softmax(-1)[0]
|
||
labels = model.config.id2label
|
||
return {labels[i]: float(probs[i]) for i in range(len(probs))}
|
||
|
||
print(emotion_scores("I just got promoted!"))
|
||
# {'joy': 0.94, 'surprise': 0.04, ...}
|
||
```
|
||
|
||
### LLM emotion classification (Claude)
|
||
```python
|
||
from anthropic import Anthropic
|
||
|
||
client = Anthropic()
|
||
PROMPT = """Classify the emotion in this text using Plutchik's 8 primary emotions
|
||
(joy, trust, fear, surprise, sadness, disgust, anger, anticipation).
|
||
Return JSON: {"primary": [...], "intensity": "low|mid|high", "dyad": "..."}.
|
||
|
||
Text: {text}"""
|
||
|
||
def classify(text):
|
||
msg = client.messages.create(
|
||
model="claude-opus-4-7",
|
||
max_tokens=200,
|
||
messages=[{"role": "user", "content": PROMPT.format(text=text)}],
|
||
)
|
||
return msg.content[0].text
|
||
```
|
||
|
||
### Wheel visualization (matplotlib polar)
|
||
```python
|
||
import matplotlib.pyplot as plt, numpy as np
|
||
|
||
emotions = ["joy", "trust", "fear", "surprise", "sadness", "disgust", "anger", "anticipation"]
|
||
colors = ["#FFEB3B", "#8BC34A", "#4CAF50", "#00BCD4", "#3F51B5", "#9C27B0", "#F44336", "#FF9800"]
|
||
angles = np.linspace(0, 2*np.pi, 8, endpoint=False)
|
||
|
||
fig, ax = plt.subplots(subplot_kw={"projection": "polar"}, figsize=(8, 8))
|
||
ax.bar(angles, [1]*8, width=2*np.pi/8, color=colors, alpha=0.7)
|
||
for a, e in zip(angles, emotions):
|
||
ax.text(a, 1.1, e, ha="center")
|
||
ax.set_yticks([]); ax.set_xticks([])
|
||
plt.show()
|
||
```
|
||
|
||
### Dyad combination
|
||
```python
|
||
def combine(e1, e2):
|
||
key = tuple(sorted([e1, e2]))
|
||
for (a, b), dyad in PRIMARY_DYADS.items():
|
||
if tuple(sorted([a, b])) == key:
|
||
return dyad
|
||
return f"{e1}+{e2} (uncommon)"
|
||
|
||
print(combine("joy", "trust")) # love
|
||
```
|
||
|
||
### GoEmotions → Plutchik mapping
|
||
```python
|
||
GOEMOTIONS_TO_PLUTCHIK = {
|
||
"admiration": "trust", "amusement": "joy", "anger": "anger",
|
||
"annoyance": "anger", "approval": "trust", "caring": "trust",
|
||
"confusion": "surprise", "curiosity": "anticipation", "desire": "anticipation",
|
||
"disappointment": "sadness", "disapproval": "disgust", "disgust": "disgust",
|
||
"embarrassment": "fear", "excitement": "joy", "fear": "fear",
|
||
"gratitude": "joy", "grief": "sadness", "joy": "joy",
|
||
"love": "joy", "nervousness": "fear", "optimism": "anticipation",
|
||
"pride": "joy", "realization": "surprise", "relief": "joy",
|
||
"remorse": "sadness", "sadness": "sadness", "surprise": "surprise",
|
||
}
|
||
```
|
||
|
||
## 매 결정 기준
|
||
| 상황 | Model |
|
||
|---|---|
|
||
| 매 structured 8-class (interpretable) | 매 Plutchik |
|
||
| 매 facial expression | 매 Ekman 6 |
|
||
| 매 fine-grained social media | 매 GoEmotions 27 |
|
||
| 매 continuous (intensity gradient) | 매 VAD dimensional |
|
||
| 매 product reviews (binary) | 매 sentiment pos/neg |
|
||
|
||
**기본값**: 매 Plutchik 8 매 emotion classification baseline (interpretable + structured).
|
||
|
||
## 🔗 Graph
|
||
- 부모: [[Affective Computing]]
|
||
- 응용: [[Sentiment-Analysis]]
|
||
|
||
## 🤖 LLM 활용
|
||
**언제**: 매 emotion taxonomy 의 prompt 매 LLM 의 give → 매 zero-shot Plutchik classification, 매 chatbot empathy module.
|
||
**언제 X**: 매 cross-cultural emotion (Plutchik 매 Western-centric), 매 micro-expression (use Ekman + AU).
|
||
|
||
## ❌ 안티패턴
|
||
- **매 mutually exclusive assumption**: 매 emotions 매 co-occur — 매 multi-label.
|
||
- **매 ignore intensity**: 매 "anger" vs "rage" 매 different.
|
||
- **매 universalism**: 매 cultural variation 매 exists (Plutchik 매 Western bias).
|
||
|
||
## 🧪 검증 / 중복
|
||
- Verified (Plutchik 1980 "A general psychoevolutionary theory of emotion", standard in affective computing literature).
|
||
- 신뢰도 A.
|
||
|
||
## 🕓 Changelog
|
||
| 날짜 | 변경 |
|
||
|---|---|
|
||
| 2026-05-08 | Phase 1 |
|
||
| 2026-05-10 | Manual cleanup — 8 primary + dyads + classifier patterns + GoEmotions mapping |
|