2nd/10_Wiki/Topic_Programming/AI_and_ML/Plutchiks-Wheel-of-Emotions.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-plutchiks-wheel-of-emotions	Plutchik's Wheel of Emotions	10_Wiki/Topics	verified	self	plutchik-wheel emotion-wheel primary-emotions	none	A	0.9	applied	emotion psychology affective-computing nlp		2026-05-10	pending	language framework Python 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

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)

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)

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)

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

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

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