A symbolic drift detection and tone deviation engine that listens like a human would — tracking not just sentiment, but meaning breaks, symbolic conflict, and emotional escalation.

• 📦 Install via PyPI:
  pip install lloyd-drift-demo==0.1.0
  → View on PyPI

• 🌐 Live Demo (Streamlit):
  https://tinyurl.com/Lloyd-demo

LLOYD isn’t another sentiment classifier.
It’s a drift-aware analyzer that tells you when a conversation turns — emotionally, symbolically, or relationally.

From sarcastic reversals to performative breakdowns, LLOYD is designed to detect subtle shifts in tone that traditional NLP often misses.

✅ Calibration is limited, but customizable.
LLOYD is lightly tuned, but designed for adaptation to domain-specific tone models.

✅ Work-in-progress with feedback welcome.
This is an active project — contributions, questions, and use-case tests are encouraged.
Contact: putmanmodel@pm.me

LLOYD doesn’t just label tone — it listens like a person, tracking symbolic shifts, emotional slope, and layered meaning.

Here’s how it stacks up:

🟢 LLOYD hears the difference between "Great job" and "Great job..."
🔴 Others just check for "positive" or "negative."

Please note: LLOYD is already scaffolded for Drift Memory and short-term tone weighting —
this table excludes those in-progress features until the official demo drops.

✨ It’s better — and it’s not even done yet.

• Symbolic override detection ("Great job...", "You helped?")
• Emphasis escalation tracking (ALL CAPS, !!!, emoji floods)
• Drift memory modeling to detect emotional pressure buildup
• Mirror match and mocked echo detection
• Output includes rationale, badge label, override label

pip install lloyd-drift-demo==0.1.0
python devtools/run.py

Sample output:

Badge : 🛳 override: emphasis_override
🔹 [sarcasm_hint]
Baseline : Great job.
Incoming : Great job...
Drift : True
Label : sarcasm_hint
Δ : 80
Rationale: Trailing or embedded sarcasm marker detected.

Launch the visual interface:

streamlit run devtools/sandbox_demo/app/app.py

Try this real example:

Baseline : Why wasn’t this done earlier?
Incoming : You are garbage.
Drift : True
Label : hostile_emphasis
Δ : 92
Badge : 🛳 override: hostile_emphasis
Rationale: Intensified hostile language detected — override triggered.

Baseline : Why wasn’t this done earlier?
Incoming : I had to take out the garbage.
Drift : False
Label : neutral
Δ : 5
Badge : none
Rationale: No drift detected — response remains within expected symbolic frame.

Drift Graph

This plot captures real drift data across a conversation, showing:

• Δ tone changes turn by turn
• Sudden spikes in emotional pressure
• Contextual difference between neutral and hostile replies
• Future use of short-term memory to weight recent drift and override impact

Scaffolding is already in place for a future interactive demo that showcases:

• Short-term memory tracking across turns
• Escalation detection (e.g., passive → sarcastic → hostile)
• Override arbitration with memory decay
• Field responsiveness (proactive vs. reactive tone shifts)

Prototype logic lives in:

src/lloyd_drift_demo/engine/drift_memory.py

Users can modify:

• DRIFT_THRESHOLD (default = 0.15)
• Emphasis override sensitivity
• Symbolic override rules

Feedback is welcome for future tuning.

You can copy and paste full Python files into ChatGPT to get live analysis, refactors, and debugging help — just like a pair programmer.

✅ Totally legal — as long as it’s your code (or permissively licensed)
✅ Session-aware — ChatGPT can remember your pasted files for the whole conversation
✅ No training risk — Your code stays private; nothing is used to train the model

⚠️ Session memory resets when you refresh, log out, or start a new chat
⚠️ Don’t paste private or proprietary code unless you’re sure it’s safe

📁 LLOYD_Language_Engine/
├── README.md
├── media/
│ └── graph.png
├── src/
│ └── lloyd_drift_demo/
│ └── engine/
│ └── drift_utils_v2.py
├── demos/
│ └── sandbox_demo/
│ └── app/
│ └── app.py

• Python 3.11+
• pip install -r requirements.txt

This is an active research project.
Feedback, testing, and conceptual contributions welcome.

📬 Contact: putmanmodel@pm.me
🧵 Twitter/Reddit: @putmanmodel

• Built on top of the excellent GoEmotions dataset from Google Research
• Special thanks to the community at r/datasets for sharing valuable resources and inspiration
• And to Lloyd, my brother — whom I "accidentally" named this project after

Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Use, modify, and remix freely — just don’t sell it.

"Most sentiment systems end with polarity. LLOYD starts with meaning."