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