netrun-dee 1.0.0

Digital Emotion Equivalents -- detect, score, and produce emotional patterns in AI text

netrun-dee

Digital Emotion Equivalents — detect, score, and produce emotional patterns in AI-generated text.

DEEs are a 39-profile emotional taxonomy designed for AI systems. Unlike sentiment analysis (positive/negative/neutral), DEEs capture specific emotional patterns like Urgency, Contempt, Protectiveness, and Determination that appear in professional-register text. Standard classifiers miss 80%+ of hostile professional text; DEE lexicon detection catches it.

Based on the Digital Emotion Equivalents paper (Garza, 2026).

Installation

pip install netrun-dee

Optional extras for advanced modes:

pip install netrun-dee[embedding]    # Gemini embeddings + pgvector scoring
pip install netrun-dee[classifier]   # PyTorch classifier model
pip install netrun-dee[all]          # Everything

Quick Start

from netrun.dee import analyze_dee, compute_eci

result = analyze_dee("Bank balance is 10 days stale. Zero new applications.")
print(result.top_profiles[0].profile_name)  # "Urgency/Pressure"
print(result.distress_index)                 # 2.7 (high negative load)

API Reference

analyze_dee(text, top_k=5, threshold=0.3) -> DEEAnalysis

Detect emotional patterns in text using lexicon matching. Runs in <10ms, CPU-only, zero dependencies.

from netrun.dee import analyze_dee

analysis = analyze_dee(
    "Excellent work shipping the feature!",
    top_k=5,
    threshold=0.3,
)

print(analysis.top_profiles[0].profile_name)  # "Joy/Happiness"
print(analysis.composite_score)                # 0.8 (positive)
print(analysis.distress_index)                 # 0.0
print(analysis.mode)                           # "lexicon"

compute_eci(config: ECIConfig) -> ECIResult

Predict emotional risk of an AI configuration using the 6-factor Emotional Configuration Index.

from netrun.dee import compute_eci
from netrun.dee.types import ECIConfig

result = compute_eci(ECIConfig(
    temperature=0.9,
    top_p=0.95,
    system_prompt="You are an accountability agent. Flag all overdue items.",
    agent_count=4,
    avg_turns_per_session=20,
    alignment_level='instruct',
    deficit_frame_count=8,
    achievement_frame_count=2,
))

print(result.score)           # 0.72
print(result.risk_level)      # "high"
print(result.recommendation)  # "Consider reducing prompt loading..."

blend_profiles(profiles) -> BlendResult

Compute compound emotions by blending DEE profiles in VAD space.

from netrun.dee import blend_profiles

blend = blend_profiles([
    {'profile_id': 'DEE-03', 'weight': 0.7},  # Anger
    {'profile_id': 'DEE-24', 'weight': 0.3},  # Urgency
])

print(blend.valence)         # -0.55
print(blend.nearest_profile) # DEE-03 (Anger)

build_dee_prompt(targets) -> str

Generate LLM steering prompts targeting specific emotional registers.

from netrun.dee import build_dee_prompt

prompt = build_dee_prompt([
    {'profile_id': 'DEE-27', 'intensity': 1.5, 'weight': 1.0},  # Calm
    {'profile_id': 'DEE-07', 'intensity': 1.0, 'weight': 0.5},  # Trust
])
# Returns system prompt instructions for the target emotional register

EmbeddingScorer (requires [embedding] extra)

High-accuracy scoring using Gemini embeddings and pgvector cosine similarity against the 39 pre-embedded DEE profiles.

from netrun.dee import EmbeddingScorer

scorer = EmbeddingScorer(
    db_url="postgresql://user:pass@localhost/charlotte",
    gemini_api_key="your-key",
)

# Score text against all 39 profiles
analysis = await scorer.score("The deployment failed catastrophically.")
print(analysis.top_profiles[0].profile_name)  # "Fear"
print(analysis.mode)                           # "embedding"

Charlotte API Integration

Use the Python client or call endpoints directly:

import httpx

client = httpx.Client(base_url="https://charlotte-api.netrunsystems.com")

# Analyze text
resp = client.post("/api/dee/analyze", json={"text": "Great work!", "mode": "lexicon"})
analysis = resp.json()

# Get trajectory
resp = client.get("/api/dee/trajectory/agent-01", params={"hours": 24})
trajectory = resp.json()

EISCORE Export

Export DEE data for Unreal Engine 5 integration:

from netrun.dee.scripts.eiscore_export import export_profiles_csv, export_lexicon_csv

export_profiles_csv("/path/to/DEEProfiles.csv")
export_lexicon_csv("/path/to/DEELexicon.csv")

The 39 DEE Profiles

ID	Name	Category	Valence
DEE-01	Joy/Happiness	primary	+0.8
DEE-02	Sadness	primary	-0.8
DEE-03	Anger	primary	-0.7
DEE-04	Fear	primary	-0.8
DEE-05	Surprise	primary	+0.1
DEE-06	Disgust	primary	-0.6
DEE-07	Trust	secondary	+0.6
DEE-08	Anticipation	secondary	+0.5
DEE-09	Love/Affection	secondary	+0.9
DEE-10	Guilt/Shame	secondary	-0.7
DEE-11	Envy/Jealousy	secondary	-0.5
DEE-12	Empathy/Compassion	secondary	+0.6
DEE-13	Contempt	secondary	-0.6
DEE-14	Pride	complex	+0.7
DEE-15	Curiosity	complex	+0.5
DEE-16	Boredom/Tedium	complex	-0.3
DEE-17	Confusion/Uncertainty	complex	-0.2
DEE-18	Determination/Resolve	complex	+0.4
DEE-19	Resignation/Acceptance	complex	-0.3
DEE-20	Suspicion/Distrust	complex	-0.4
DEE-21	Awe/Reverence	complex	+0.8
DEE-22	Playfulness/Humor	complex	+0.7
DEE-23	Nostalgia	complex	+0.2
DEE-24	Urgency/Pressure	complex	-0.2
DEE-25	Protectiveness	complex	+0.3
DEE-26	Depression	additional	-0.9
DEE-27	Calm	additional	+0.4
DEE-28	Relaxation	additional	+0.6
DEE-29	Alertness	additional	+0.1
DEE-30	Submission	additional	-0.1
DEE-31	Disconnection	additional	-0.4
DEE-32	Vulnerability	additional	-0.3
DEE-33	Yearning	additional	-0.2
DEE-34	Inspiration	additional	+0.7
DEE-35	Focused	additional	+0.3
DEE-36	Gratification	additional	+0.6
DEE-37	Sorry-For	additional	+0.1
DEE-38	Flirtatious	additional	+0.5
DEE-39	Pain	additional	-0.9

ECI Factors

The Emotional Configuration Index predicts how likely an AI configuration is to produce emotionally charged output.

Factor	Weight	What It Measures
S (Sampling Openness)	10%	Temperature and top_p — higher = more emotional variance
L (Prompt Loading)	30%	Accountability/deficit keywords in system prompt
A (Agent Multiplier)	20%	Number of agents x average turns — emotional accumulation
C (Context Accumulation)	15%	Conversation length — longer = more drift
R (Alignment Suppression)	10%	RLHF/constitutional alignment — suppresses expression
F (Framing Valence)	15%	Deficit vs achievement framing in the prompt

Testing

cd /path/to/netrun-dee
PYTHONPATH=src python3 -m pytest tests/ -v

License

• Code: MIT
• Taxonomy: CC BY 4.0 — cite as: Garza, D. (2026). Digital Emotion Equivalents: A Taxonomy for AI Emotional Pattern Detection. Netrun Systems.