🧠⚡ Emotion Analysis — Neuro-Symbolic

"Ἡ Μήδεια τὰ πάθη νοητικῷ τε καὶ συμβολικῷ λόγῳ κρίνει"
Medea judges emotions through both neural and symbolic reasoning

Harness the power of neuro-symbolic AI to analyze emotions with unprecedented depth. The system combines LLM-powered neural extraction with formal symbolic reasoning to validate, adjust, and explain emotional interpretations according to classical frameworks.

⚙️ Analysis Configuration

🔍 Analysis Mode

Normal (Basic LLM Extraction)

Cairns (Enhanced Framework)

⚡ Cairns NeSy1 (Python Heuristic Grammar, 7 families)

🧠 Cairns NeSy2 (pyDatalog, 12 families, transitive inference + live script-label check)

🌐 Cairns NeSy3 (semantic grammar, closed-set ontology, language-independent — Recommended)

🗣️ Cairns NeSy4 (dialogic grammar: negotiation + critic + patch-queue — Experimental)

📚 Genre Context

🤖 AI Provider

✓ Options

Enable Feedback Loop

🌍 Language

📝 Text for Emotion Analysis

Minimum 20 characters. Include rich emotional language for best symbolic reasoning results.

💀 Analysis Failed

🧠 Analysis Results

Total Emotions

—

Dominant Emotion

Grammar Compliance

—

Analysis Mode

Node Colors (Sentiment)

Very Positive (0.5–1.0)

Positive (0.1–0.5)

Neutral (−0.1–0.1)

Negative (−0.5–−0.1)

Very Negative (−1.0–−0.5)

Node Sizes

Size = Emotion Intensity

🧠⚡ Analyzing Emotions

Performing neuro-symbolic reasoning and validating emotions...

🏛️ API Core Configuration