Understand not just WHAT people say — but HOW, WHY, and WITH WHAT EMOTION they say it.
🚧 ARGUFORMER IS CURRENTLY UNDER ACTIVE DEVELOPMENT 🚧
🔨 Building the next generation of AI-powered argument & communication analysis...
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Current Status: 🟡 Under Construction
ArguFormer is an evolving NLP-based communication intelligence system designed to analyze human arguments and conversations from multiple dimensions.
Instead of simply asking:
"Is this argument correct?"
ArguFormer aims to ask:
"What is being argued, how is it being argued, what emotions are involved, and how does the conversation evolve?"
The long-term goal is to build a system capable of analyzing:
- 🧠 Logical fallacies
- 💬 Argument quality
- 🎭 Emotional patterns
- 😊 Sentiment
- ☣️ Toxicity
- 🎯 Stance
- 🔥 Emotional escalation
- 🔎 Evidence & contextual support
- 🌍 Multilingual conversations
- 📊 Speaker-level communication patterns
ArguFormer is being developed incrementally as a modular NLP system.
- 🧹 Text preprocessing
- 👥 Speaker segmentation
- 📚 TF-IDF + Logistic Regression fallacy detection
- 🤖 Transformer-based fallacy analysis
- 🎭 GoEmotions-based emotion detection
- 🔗 Emotion analysis integrated with speaker segments
- 📈 Model evaluation & latency benchmarking
- 📝 Logging & exception handling
- ⚙️ Configurable project structure
- 🎭 Emotion evaluation & threshold tuning
- 📊 Unified analysis output
- 🧠 Argument-quality scoring
- 💬 Sentiment analysis
- ☣️ Toxicity detection
- 🔎 Retrieval-Augmented Generation (RAG)
- 🌍 Multilingual analysis
- 📈 Communication & emotional timelines
- 🎙️ Speech-to-text conversation analysis
- ⚡ Real-time analysis
- 📊 Interactive communication dashboard
- 🧑🤝🧑 Speaker behavior profiling
- 🔥 Emotional escalation detection
- 🧠 Evidence-aware reasoning
- 🤖 AI-powered communication feedback
- 📝 Debate & meeting intelligence
┌─────────────────────┐
│ Debate / Text │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Preprocessing │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Speaker Segmentation│
└──────────┬──────────┘
│
┌────────────────┼────────────────┐
│ │ │
▼ ▼ ▼
🧠 Fallacy 🎭 Emotion 😊 Sentiment
Analysis Analysis Analysis
│ │ │
└────────────────┼────────────────┘
│
▼
┌─────────────────────┐
│ Communication │
│ Intelligence Layer │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ Reports / Dashboard │
└─────────────────────┘
ArguFormer is being designed as a modular pipeline, where individual NLP capabilities can be developed, evaluated, and replaced independently.
Input
│
▼
Preprocessing
│
▼
Speaker Segmentation
│
├──► Fallacy Detection
│ ├── Classical ML
│ └── Transformer
│
├──► Emotion Detection
│ └── RoBERTa + GoEmotions
│
├──► Sentiment Analysis
│
├──► Toxicity Detection
│
└──► Future NLP Modules
│
▼
Aggregation
│
▼
Communication Intelligence
One of the newest additions to ArguFormer is emotion analysis using:
SamLowe/roberta-base-go_emotions
The model analyzes 28 emotion categories, allowing ArguFormer to distinguish between signals such as:
anger
annoyance
approval
disapproval
fear
nervousness
sadness
admiration
confusion
...
Instead of reducing communication to:
Positive / Negative / Neutral
ArguFormer aims to understand the emotional nuance behind an argument.
For example:
"I completely disagree with your argument."
→ disapproval
while:
"I'm really worried about what will happen."
→ nervousness
→ fear
This will eventually contribute to emotional escalation and communication-pattern analysis.
- Python
- Scikit-learn
- Hugging Face Transformers
- PyTorch
- TF-IDF
- Logistic Regression
- RoBERTa
- GoEmotions
- Modular Python architecture
- CLI interface
- YAML configuration
- Logging
- Custom exceptions
- Evaluation & benchmarking
- FAISS
- Sentence Transformers
- RAG
- Whisper
- FastAPI
- Streamlit
- Real-time processing
Arguformer/
│
├── cli/
├── configs/
├── core/
│ ├── preprocessing.py
│ ├── segmentation.py
│ ├── emotion_analyzer.py
│ └── ...
│
├── models/
│ ├── fallacy_lr.py
│ ├── fallacy_transformer.py
│ └── ...
│
├── datasets/
├── evaluation/
├── rag/
├── tests/
├── utils/
├── data/
├── outputs/
└── main.py
ArguFormer is being built using an incremental approach:
Experiment
↓
Validate
↓
Modularize
↓
Integrate
↓
Evaluate
↓
Improve
The goal isn't to throw dozens of AI models together.
The goal is to build a system where each component has a clear purpose, measurable behavior, and defined interface.
PHASE 1 ─ Core NLP
████████████████████████████ ✅
PHASE 2 ─ Communication Intelligence
████████████████░░░░░░░░░░░░ 🔨
PHASE 3 ─ RAG & Evidence Intelligence
██████░░░░░░░░░░░░░░░░░░░░░░ 🔜
PHASE 4 ─ Real-Time Intelligence
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PHASE 5 ─ Full Communication Platform
░░░░░░░░░░░░░░░░░░░░░░░░░░░ 🔮
| Component | Status |
|---|---|
| Project Architecture | 🟢 |
| Preprocessing | 🟢 |
| Speaker Segmentation | 🟢 |
| Classical Fallacy Model | 🟢 |
| Transformer Fallacy Model | 🟢 |
| Emotion Detection | 🟢 |
| Emotion Integration | 🟢 |
| Emotion Evaluation | 🟡 |
| Argument Quality | 🔨 |
| Sentiment Intelligence | 🔜 |
| Toxicity Analysis | 🔜 |
| RAG | 🔜 |
| Multilingual NLP | 🔜 |
| Real-Time Analysis | 🔮 |
| Interactive Dashboard | 🔮 |
Because ArguFormer isn't finished.
And that's intentional.
This repository documents the development of the system as it evolves from:
an argument/fallacy analyzer
into:
a broader AI communication-intelligence platform.
Expect experiments, refactoring, new models, benchmark improvements, architectural changes, and probably a few bugs along the way. 😄
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