Advanced Conversation Analytics Suite - Issue #4

Status: ✅ COMPLETED - Foundational analytics for Issue #3 code extraction

🎯 Overview

This analytics suite provides comprehensive word frequency analysis and conversational pattern detection capabilities for ChatGPT and Claude conversation exports. It serves as the foundational research for Issue #3’s code extraction system.

📊 Analytics Capabilities

Core Analysis Scripts

Script Purpose Key Features
conversation_context_analyzer.py Word frequency & code patterns 15+ regex patterns, threading analysis, topic emergence
python_word_analyzer.py Python-specific conversations Language detection, technical term extraction
frequency_conversation_finder.py Frequency analysis topics Identifies conversations about frequency analysis
you_vs_your_analyzer.py Pronoun usage patterns Role-based analysis, platform comparison
technical_discussion_analyzer.py Technical complexity scoring Basic technical metrics
improved_technical_analyzer.py Enhanced technical metrics Weighted scoring (code blocks: 10x, functions: 8x)
final_technical_insights.py Comprehensive insights Statistical analysis and correlations

Output Files Generated

Data Files (CSV)

Interactive Dashboards (HTML)

Reports (Markdown)

Text Extracts

🔬 Key Research Findings

1. Word Frequency Patterns

2. Revolutionary Pronoun Usage Insights

3. Technical Discussion Classification

🛠️ Setup & Installation

Dependencies

Install all required dependencies:

pip install -r requirements.txt

Required packages: - plotly>=5.0.0 - Interactive visualizations - pandas>=1.5.0 - Data manipulation - beautifulsoup4>=4.11.0 - HTML parsing (legacy scripts) - requests>=2.28.0 - HTTP requests (legacy scripts)

Data Configuration

The analytics scripts support flexible file configuration:

Method 1: Command Line Arguments

python src/conversation_context_analyzer.py chatgpt.json claude.json
python src/you_vs_your_analyzer.py chatgpt.json claude.json

Method 2: Environment Variables

export CHATGPT_CONVERSATIONS_FILE=path/to/chatgpt.json
export CLAUDE_CONVERSATIONS_FILE=path/to/claude.json
python src/conversation_context_analyzer.py

Method 3: Default Data Directory

mkdir data/
# Place your files as:
# data/chatgpt_conversations.json
# data/claude_conversations.json
python src/conversation_context_analyzer.py

Data Format Requirements

ChatGPT Format

[
  {
    "id": "conversation_id",
    "mapping": {
      "node_id": {
        "message": {
          "content": {"parts": ["message text"]},
          "create_time": 1234567890,
          "author": {"role": "user"}
        }
      }
    }
  }
]

Claude Format

[
  {
    "uuid": "conversation_id",
    "created_at": "2024-01-01T00:00:00Z",
    "chat_messages": [
      {
        "text": "message text",
        "sender": "human",
        "created_at": "2024-01-01T00:00:00Z"
      }
    ]
  }
]

🚀 Running Analytics

Complete Analytics Workflow

Run in this recommended order for full analysis:

# 1. Core context analysis
python src/conversation_context_analyzer.py

# 2. Python-specific analysis  
python src/python_word_analyzer.py

# 3. Frequency analysis identification
python src/frequency_conversation_finder.py

# 4. Pronoun usage analysis
python src/you_vs_your_analyzer.py

# 5. Technical complexity analysis
python src/technical_discussion_analyzer.py
python src/improved_technical_analyzer.py

# 6. Final insights generation
python src/final_technical_insights.py

Validation & Testing

Test the analytics pipeline:

python src/analytics_validator.py

This validator checks: - ✅ All modules import successfully - ✅ Required dependencies are available - ✅ Basic functionality works - ✅ Expected output files exist

🔗 Integration with Issue #3

This analytics suite directly enables Issue #3 (Code Extraction) by providing:

1. Conversation Filtering Capabilities

# Technical complexity scoring for prioritization
technical_score = (
    code_blocks * 10 + function_defs * 8 + imports * 6 + 
    assignments * 3 + file_refs * 5 + cli_commands * 4
) / word_count * 1000

2. Language Detection Foundation

3. Quality Scoring System

4. Pronoun Pattern Analysis for Code Context

📈 Performance Metrics

The analytics suite processes: - 2,254 conversations analyzed across 27 months - 50K+ messages processed with streaming efficiency - 15+ technical patterns identified with weighted scoring - Memory-efficient processing using generators for large datasets

Streaming Architecture

def generate_timeline_streaming(self) -> Generator[Dict[str, Any], None, None]:
    # Memory-efficient processing for 1000+ conversations
    for conv in self.load_data_streaming():
        yield self.process_single_conversation(conv)

🎯 Next Steps for Issue #3

Immediate Applications

  1. Use technical scoring to prioritize conversations for code extraction
  2. Apply pronoun analysis to identify user-specific vs general code examples
  3. Leverage platform differences for extraction strategy optimization
  4. Use frequency analysis to identify domain-specific code conversations

Enhanced Code Extraction Strategy

def prioritize_code_extraction(conversations):
    # Priority extraction based on analytics findings:
    # 1. High technical complexity score (>400)
    # 2. Strong "your" preference (indicates user code)
    # 3. Python/web development conversations
    # 4. ChatGPT platform (more possessive code references)

📊 Research Methodology

🏆 Achievements

Issue Status: ✅ COMPLETED - Ready for Issue #3 code extraction implementation

This comprehensive analytics foundation provides the necessary conversation filtering, quality scoring, and pattern detection capabilities to enable effective code extraction from AI conversation data.