---
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]] · [[Emotion-Theory]]
- 변형: [[Ekman-Basic-Emotions]] · [[GoEmotions]] · [[VAD-Model]]
- 응용: [[Sentiment-Analysis]] · [[Emotion-Classification]] · [[Chatbot-Empathy]]
- Adjacent: [[Mental-Health-AI]] · [[Conversational-AI]]

## 🤖 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 |
