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<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Email Classification System - Technical Guide</title>
<style>
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</style>
</head>
<body>
<div class="container">
<div class="header animate-fade-in">
<h1>📧 Email Classification System</h1>
<p>Advanced AI-Powered Email Categorization & Analysis</p>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>System Overview</h2>
<p>
This advanced email classification system uses modern machine learning techniques to
automatically categorize emails into 10 distinct categories. Built with transformers,
ensemble models, and GPU acceleration, it provides high-accuracy classification along with
emotional analysis.
</p>
<div class="info">
<strong>Key Features:</strong> GPU-accelerated processing, Transformer models, Ensemble
learning, Emotional analysis, Real-time classification
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>How Classification Works</h2>
<div class="flow-diagram">
<div class="flow-step">
<h4>📥 Email Input</h4>
<p>Subject, body, metadata collected</p>
</div>
<div class="flow-step">
<h4>🧹 Text Preprocessing</h4>
<p>Clean HTML, URLs, normalize text</p>
</div>
<div class="flow-step">
<h4>🔢 Feature Extraction</h4>
<p>Sentence transformers + metadata</p>
</div>
<div class="flow-step">
<h4>🤖 ML Classification</h4>
<p>Ensemble model prediction</p>
</div>
<div class="flow-step">
<h4>🎯 Zero-Shot Backup</h4>
<p>Transformer-based classification</p>
</div>
<div class="flow-step">
<h4>💭 Emotion Analysis</h4>
<p>Sentiment & emotional scoring</p>
</div>
</div>
<h3>Technical Process Details</h3>
<div class="usage-example">
<h4>1. Text Preprocessing</h4>
<div class="code-block">
def preprocess_text(self, text: str) -> str: text = str(text).lower() text =
re.sub(r'<[^>]+>', '', text) # Remove HTML text = re.sub(r'\S+@\S+\.\S+',
'[EMAIL]', text) # Replace emails text = re.sub(r'http\S+|www\S+', '[URL]', text) #
Replace URLs return re.sub(r'\s+', ' ', text).strip()
</div>
</div>
<div class="usage-example">
<h4>2. Feature Extraction</h4>
<p>The system extracts two types of features:</p>
<ul style="margin: 10px 0 10px 20px">
<li>
<strong>Text Embeddings:</strong> Using SentenceTransformer 'all-MiniLM-L6-v2' model
</li>
<li>
<strong>Metadata Features:</strong> Subject length, body length, attachments,
recipients, send time, emotion scores
</li>
</ul>
</div>
<div class="usage-example">
<h4>3. Ensemble Classification</h4>
<p>
Combines Random Forest (200 trees) + Logistic Regression with soft voting for robust
predictions.
</p>
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>Email Categories</h2>
<p>The system classifies emails into 10 comprehensive categories:</p>
<div class="categories-grid">
<div class="category-card">
<h4>📋 Strategic Planning</h4>
<p>Long-term business strategy, corporate planning, acquisitions</p>
<div class="keywords">
<span class="keyword">strategy</span>
<span class="keyword">planning</span>
<span class="keyword">acquisition</span>
<span class="keyword">merger</span>
<span class="keyword">vision</span>
</div>
</div>
<div class="category-card">
<h4>⚙️ Daily Operations</h4>
<p>Day-to-day operations, routine tasks, procedures</p>
<div class="keywords">
<span class="keyword">operations</span>
<span class="keyword">daily</span>
<span class="keyword">routine</span>
<span class="keyword">workflow</span>
</div>
</div>
<div class="category-card">
<h4>💰 Financial</h4>
<p>Budget, accounting, financial reports, expenses</p>
<div class="keywords">
<span class="keyword">budget</span>
<span class="keyword">accounting</span>
<span class="keyword">revenue</span>
<span class="keyword">profit</span>
</div>
</div>
<div class="category-card">
<h4>⚖️ Legal & Compliance</h4>
<p>Legal matters, regulatory compliance, contracts</p>
<div class="keywords">
<span class="keyword">legal</span>
<span class="keyword">compliance</span>
<span class="keyword">contract</span>
<span class="keyword">policy</span>
</div>
</div>
<div class="category-card">
<h4>🤝 Client & External</h4>
<p>External communications, client relations, partnerships</p>
<div class="keywords">
<span class="keyword">client</span>
<span class="keyword">customer</span>
<span class="keyword">partner</span>
<span class="keyword">vendor</span>
</div>
</div>
<div class="category-card">
<h4>👥 HR & Personnel</h4>
<p>Human resources, hiring, employee matters</p>
<div class="keywords">
<span class="keyword">hr</span>
<span class="keyword">hiring</span>
<span class="keyword">employee</span>
<span class="keyword">recruitment</span>
</div>
</div>
<div class="category-card">
<h4>📅 Meetings & Events</h4>
<p>Meeting scheduling, event planning, appointments</p>
<div class="keywords">
<span class="keyword">meeting</span>
<span class="keyword">schedule</span>
<span class="keyword">event</span>
<span class="keyword">calendar</span>
</div>
</div>
<div class="category-card">
<h4>🚨 Urgent & Critical</h4>
<p>Time-sensitive, emergency, critical issues</p>
<div class="keywords">
<span class="keyword">urgent</span>
<span class="keyword">emergency</span>
<span class="keyword">asap</span>
<span class="keyword">deadline</span>
</div>
</div>
<div class="category-card">
<h4>💬 Personal & Informal</h4>
<p>Personal communications, informal chats</p>
<div class="keywords">
<span class="keyword">personal</span>
<span class="keyword">casual</span>
<span class="keyword">chat</span>
<span class="keyword">social</span>
</div>
</div>
<div class="category-card">
<h4>💻 Technical & IT</h4>
<p>Technical issues, IT support, system problems</p>
<div class="keywords">
<span class="keyword">technical</span>
<span class="keyword">system</span>
<span class="keyword">software</span>
<span class="keyword">network</span>
</div>
</div>
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>API Endpoints</h2>
<p>The system provides RESTful API endpoints for various classification tasks:</p>
<div class="api-endpoint">
<span class="method">GET</span>/classify/email/<email_id>
<p style="margin-top: 10px; opacity: 0.9">Classify a single email by ID</p>
</div>
<div class="api-endpoint">
<span class="method">POST</span>/classify/batch
<p style="margin-top: 10px; opacity: 0.9">Classify multiple emails in batch</p>
</div>
<div class="api-endpoint">
<span class="method">POST</span>/classify/folder/<username>/<folder>
<p style="margin-top: 10px; opacity: 0.9">Classify all emails in a specific folder</p>
</div>
<div class="api-endpoint">
<span class="method">POST</span>/classify/analyze/text
<p style="margin-top: 10px; opacity: 0.9">Analyze text for emotional content</p>
</div>
<div class="api-endpoint">
<span class="method">POST</span>/classify/train
<p style="margin-top: 10px; opacity: 0.9">Train the classifier on Enron dataset</p>
</div>
<div class="api-endpoint">
<span class="method">GET</span>/classify/model/status
<p style="margin-top: 10px; opacity: 0.9">Check if the model is trained and ready</p>
</div>
<h3>Example API Usage</h3>
<div class="code-block">
# Single email classification curl -X GET http://localhost:5000/classify/email/123 # Batch
classification curl -X POST http://localhost:5000/classify/batch \ -H "Content-Type:
application/json" \ -d '[{"subject": "Meeting tomorrow", "body": "Can we schedule..."}]' #
Train model curl -X POST http://localhost:5000/classify/train \ -H "Content-Type:
application/json" \ -d '{"enron_dir": "/path/to/enron.db", "max_emails": 5000}'
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>Technology Stack</h2>
<div class="tech-stack">
<div class="tech-item">🤖 Transformers</div>
<div class="tech-item">🧠 SentenceTransformers</div>
<div class="tech-item">🌲 Random Forest</div>
<div class="tech-item">📊 Logistic Regression</div>
<div class="tech-item">🚀 GPU Acceleration</div>
<div class="tech-item">🐍 Python Flask</div>
<div class="tech-item">📦 scikit-learn</div>
<div class="tech-item">🔥 PyTorch</div>
</div>
<div class="warning">
<strong>Device Support:</strong> The system automatically detects and uses the best
available device:
<ul style="margin: 10px 0 0 20px">
<li>CUDA GPU (preferred for best performance)</li>
<li>Apple Metal Performance Shaders (MPS)</li>
<li>CPU fallback (if no GPU available)</li>
</ul>
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>Performance & Features</h2>
<div class="performance-metrics">
<div class="metric">
<h4>⚡</h4>
<p>GPU Accelerated</p>
</div>
<div class="metric">
<h4>🎯</h4>
<p>High Accuracy</p>
</div>
<div class="metric">
<h4>📊</h4>
<p>Batch Processing</p>
</div>
<div class="metric">
<h4>💾</h4>
<p>Model Persistence</p>
</div>
<div class="metric">
<h4>📈</h4>
<p>Emotion Analysis</p>
</div>
<div class="metric">
<h4>🔄</h4>
<p>Real-time Classification</p>
</div>
</div>
<h3>Emotional Analysis Features</h3>
<ul style="margin: 15px 0 15px 20px">
<li><strong>Sentiment Analysis:</strong> Polarity and subjectivity scoring</li>
<li><strong>Stress Detection:</strong> Identifies stressed communication patterns</li>
<li><strong>Tone Analysis:</strong> Formal, casual, sarcastic tone detection</li>
<li><strong>Dominant Emotion:</strong> Overall emotional classification</li>
</ul>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>Training Process</h2>
<p>The system can be trained on the Enron email dataset with advanced analytics:</p>
<div class="usage-example">
<h4>Training Steps:</h4>
<ol style="margin: 10px 0 10px 20px">
<li><strong>Data Loading:</strong> Load emails from SQLite database</li>
<li><strong>Dataset Analysis:</strong> Comprehensive data quality assessment</li>
<li>
<strong>Zero-shot Labeling:</strong> Initial categorization using embedding similarity
</li>
<li>
<strong>Feature Engineering:</strong> Extract text embeddings + metadata features
</li>
<li><strong>Model Training:</strong> Train ensemble model with cross-validation</li>
<li><strong>Evaluation:</strong> Generate classification reports and metrics</li>
<li><strong>Model Persistence:</strong> Save trained model for future use</li>
</ol>
</div>
<div class="info">
<strong>Dataset Analysis Features:</strong>
<ul style="margin: 10px 0 0 20px">
<li>Label distribution analysis</li>
<li>Data quality checks (null/empty content)</li>
<li>Text length statistics</li>
<li>Category sufficiency validation</li>
<li>Automated recommendations</li>
</ul>
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>Usage Examples</h2>
<h3>1. Training the Model</h3>
<div class="code-block">
# First, analyze the dataset curl -X POST http://localhost:5000/classify/train \ -H
"Content-Type: application/json" \ -d '{ "enron_dir": "/path/to/enron.db", "max_emails":
5000, "analyze_only": true }' # Then train the model curl -X POST
http://localhost:5000/classify/train \ -H "Content-Type: application/json" \ -d '{
"enron_dir": "/path/to/enron.db", "max_emails": 5000, "analyze_only": false }'
</div>
<h3>2. Classifying Emails</h3>
<div class="code-block">
# Single email classification import requests response =
requests.get('http://localhost:5000/classify/email/123') result = response.json()
print(f"Category: {result['classification']['category_name']}") print(f"Confidence:
{result['classification']['confidence']:.2f}") print(f"Emotion:
{result['classification']['emotion']}")
</div>
<h3>3. Batch Processing</h3>
<div class="code-block">
emails = [ { "subject": "Quarterly Budget Review", "body": "Please review the attached
budget proposals...", "sender": "finance@company.com" }, { "subject": "Team Meeting -
Tomorrow 2 PM", "body": "Don't forget our weekly team meeting...", "sender":
"manager@company.com" } ] response = requests.post(
'http://localhost:5000/classify/batch', json=emails )
</div>
</div>
<div class="section animate-fade-in">
<h2><span class="icon"></span>System Requirements & Setup</h2>
<div class="warning">
<strong>Hardware Requirements:</strong>
<ul style="margin: 10px 0 0 20px">
<li>GPU with CUDA support (recommended) or Apple Silicon for MPS</li>
<li>Minimum 8GB RAM (16GB+ recommended for large datasets)</li>
<li>SSD storage for model files and datasets</li>
</ul>
</div>
<div class="info">
<strong>Software Dependencies:</strong>
<ul style="margin: 10px 0 0 20px">
<li>Python 3.8+</li>
<li>PyTorch with GPU support</li>
<li>Transformers library</li>
<li>SentenceTransformers</li>
<li>scikit-learn</li>
<li>Flask for API endpoints</li>
</ul>
</div>
</div>
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