corpus_analysis.py - ResonanceOS v6 Documentation

Corpus Analysis Thesis

The corpus_analysis.py module demonstrates comprehensive text corpus analysis capabilities of ResonanceOS v6, including HRV pattern analysis, statistical insights, quality assessment, and data-driven recommendations. This advanced data science example covers single document analysis, batch processing, corpus-level insights, improvement area identification, and visualization data generation - all designed to help researchers, content managers, and data scientists understand and optimize large text collections using HRV-based metrics and human resonance analysis.

Technical Specifications

Analysis Types: Single Document, Batch Processing, Corpus-Level Insights
Metrics: HRV Analysis, Quality Assessment, Style Classification
Features: Statistical Analysis, Pattern Recognition, Recommendations
Export: JSON Results, Visualization Data
Applications: Research, Content Management, Quality Control

Core Implementation Architecture

class CorpusAnalyzer:
    """Advanced corpus analysis using ResonanceOS v6"""
    
    def __init__(self):
        self.writer = HumanResonantWriter()
        self.extractor = HRVExtractor()
        
        # HRV dimension names
        self.dimensions = [
            "sentence_variance", "emotional_valence", "emotional_intensity",
            "assertiveness_index", "curiosity_index", "metaphor_density",
            "storytelling_index", "active_voice_ratio"
        ]

def analyze_single_document(self, text: str, metadata: Dict[str, Any] = None) -> Dict[str, Any]:
    """Analyze a single document comprehensively"""
    
    # Extract HRV vector
    hrv_vector = self.extractor.extract(text)
    
    # Basic text statistics
    words = text.split()
    sentences = text.split('.')
    paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
    
    analysis = {
        "hrv_vector": hrv_vector,
        "avg_hrv_score": sum(hrv_vector) / len(hrv_vector),
        "text_statistics": {
            "word_count": len(words),
            "sentence_count": len([s for s in sentences if s.strip()]),
            "paragraph_count": len(paragraphs),
            "avg_sentence_length": len(words) / len([s for s in sentences if s.strip()]) if sentences else 0,
            "avg_word_length": sum(len(word) for word in words) / len(words) if words else 0
        },
        "quality_metrics": self.calculate_quality_metrics(hrv_vector),
        "style_analysis": self.analyze_writing_style(hrv_vector),
        "metadata": metadata or {}
    }
                

Single Document Analysis

Comprehensive analysis of individual documents with HRV metrics

Batch Processing

Efficient analysis of large document collections

Quality Assessment

Multi-dimensional quality evaluation and scoring

Style Classification

Writing style identification and characteristics analysis

Corpus Analysis Workflow

1. Document Processing

Extract HRV vectors and basic text statistics

↓

2. Quality Assessment

Calculate comprehensive quality metrics and scores

↓

3. Style Analysis

Identify writing styles and characteristics

↓

4. Corpus Insights

Generate aggregate insights and recommendations

Quality Metrics System

Comprehensive Quality Assessment

def calculate_quality_metrics(self, hrv_vector: List[float]) -> Dict[str, Any]:
    """Calculate comprehensive quality metrics"""
    
    avg_score = sum(hrv_vector) / len(hrv_vector)
    
    # Quality assessment
    if avg_score > 0.8:
        overall_quality = "Excellent"
        quality_score = 95
    elif avg_score > 0.7:
        overall_quality = "Good"
        quality_score = 85
    elif avg_score > 0.6:
        overall_quality = "Fair"
        quality_score = 75
    else:
        overall_quality = "Poor"
        quality_score = 65
    
    # Engagement potential
    engagement_score = (hrv_vector[1] + hrv_vector[2] + hrv_vector[4] + hrv_vector[6]) / 4
    
    # Clarity score
    clarity_score = (hrv_vector[0] + hrv_vector[7]) / 2
                    

Quality Classification System

Excellent

Score > 0.8 (95 points)

Good

Score > 0.7 (85 points)

Fair

Score > 0.6 (75 points)

Poor

Score ≤ 0.6 (65 points)

Quality Dimensions

Engagement Score

Combination of emotional valence, intensity, curiosity, and storytelling

Clarity Score

Sentence variance and active voice ratio assessment

Dimension Quality

Individual HRV dimension strength assessment

Recommendations

Specific improvement suggestions based on analysis

Writing Style Analysis

Style Classification & Characteristics

def analyze_writing_style(self, hrv_vector: List[float]) -> Dict[str, Any]:
    """Analyze writing style characteristics"""
    
    # Determine primary style
    if hrv_vector[6] > 0.6 and hrv_vector[5] > 0.5:
        primary_style = "Creative/Narrative"
    elif hrv_vector[3] > 0.6 and hrv_vector[7] > 0.6:
        primary_style = "Professional/Direct"
    elif hrv_vector[1] > 0.5 and hrv_vector[2] > 0.5:
        primary_style = "Emotional/Persuasive"
    elif hrv_vector[4] > 0.5:
        primary_style = "Inquisitive/Analytical"
    else:
        primary_style = "Balanced/Neutral"
    
    return {
        "primary_style": primary_style,
        "characteristics": style_characteristics,
        "formality_level": "Formal" if hrv_vector[3] > 0.6 else "Informal" if hrv_vector[3] < 0.4 else "Semi-formal",
        "engagement_level": "High" if sum(hrv_vector)/len(hrv_vector) > 0.7 else "Medium" if sum(hrv_vector)/len(hrv_vector) > 0.5 else "Low"
    }
                    

Writing Style Categories

Creative/Narrative

Storytelling and metaphor-rich content

Professional/Direct

Assertive and active voice communication

Emotional/Persuasive

Emotionally engaging and persuasive content

Inquisitive/Analytical

Question-driven and analytical approach

Balanced/Neutral

Well-balanced and neutral tone

Corpus-Level Insights Generation

Aggregate Analysis & Pattern Recognition

def generate_corpus_insights(self, analyses: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Generate comprehensive corpus insights"""
    
    # Overall statistics
    hrv_scores = [a['avg_hrv_score'] for a in analyses]
    word_counts = [a['text_statistics']['word_count'] for a in analyses]
    
    overall_stats = {
        "total_documents": len(analyses),
        "total_words": sum(word_counts),
        "avg_words_per_doc": sum(word_counts) / len(word_counts),
        "avg_hrv_score": sum(hrv_scores) / len(hrv_scores),
        "hrv_score_std": np.std(hrv_scores),
        "min_hrv_score": min(hrv_scores),
        "max_hrv_score": max(hrv_scores)
    }
    
    # Quality distribution
    quality_counts = Counter(a['quality_metrics']['overall_quality'] for a in analyses)
    quality_distribution = {
        "Excellent": quality_counts.get("Excellent", 0),
        "Good": quality_counts.get("Good", 0),
        "Fair": quality_counts.get("Fair", 0),
        "Poor": quality_counts.get("Poor", 0)
    }
                    

Corpus Statistics

Total Documents

0.734

Average HRV Score

0.156

HRV Score Std Dev

447

Avg Words/Document

Excellent Quality

Good Quality

Fair Quality

Poor Quality

Dimension Statistics

Sentence Variance

Mean: 0.642 | Std: 0.156 | Range: 0.421-0.823

Emotional Valence

Mean: 0.387 | Std: 0.234 | Range: 0.156-0.678

Emotional Intensity

Mean: 0.523 | Std: 0.189 | Range: 0.334-0.712

Assertiveness Index

Mean: 0.689 | Std: 0.145 | Range: 0.544-0.834

Improvement Area Identification

Pattern Recognition & Recommendations

def identify_improvement_areas(self, analyses: List[Dict[str, Any]]) -> List[str]:
    """Identify common improvement areas across the corpus"""
    
    improvement_areas = []
    
    # Analyze dimension weaknesses
    dimension_avgs = {}
    for i, dimension in enumerate(self.dimensions):
        values = [a['hrv_vector'][i] for a in analyses]
        dimension_avgs[dimension] = sum(values) / len(values)
    
    # Identify dimensions that need improvement
    for dimension, avg_value in dimension_avgs.items():
        if avg_value < 0.4:
            improvement_areas.append(f"Low {dimension.replace('_', ' ')} (avg: {avg_value:.2f})")
    
    # Quality issues
    poor_quality_count = sum(1 for a in analyses if a['quality_metrics']['overall_quality'] == 'Poor')
    if poor_quality_count > len(analyses) * 0.2:  # More than 20% poor quality
        improvement_areas.append(f"High poor quality rate ({poor_quality_count}/{len(analyses)})")
                    

Common Improvement Areas

Low Emotional Valence

Average: 0.387 - Add more positive elements

Low Curiosity Index

Average: 0.342 - Include more questions

Content Diversity

Low HRV variance - Increase variety

Engagement Issues

30% low engagement - Enhance connection

Visualization Data Generation

Data for Analytics & Reporting

def generate_visualization_data(self, insights: Dict[str, Any]) -> Dict[str, Any]:
    """Generate data for visualizations"""
    
    # Quality distribution chart data
    quality_data = {
        "labels": list(insights['quality_distribution'].keys()),
        "values": list(insights['quality_distribution'].values())
    }
    
    # Style distribution chart data
    style_data = {
        "labels": list(insights['style_distribution'].keys()),
        "values": list(insights['style_distribution'].values())
    }
    
    # Dimension radar chart data
    dimension_data = {
        "labels": [d.replace('_', ' ').title() for d in self.dimensions],
        "values": [insights['dimension_statistics'][d]['mean'] for d in self.dimensions]
    }
                    

Visualization Types

Quality Distribution

Bar chart showing quality level distribution

Style Distribution

Pie chart of writing style categories

Dimension Radar

8-dimensional HRV radar chart

Corpus-Level Recommendations

Data-Driven Improvement Strategies

def generate_corpus_recommendations(self, overall_stats: Dict, dimension_stats: Dict) -> List[str]:
    """Generate corpus-level recommendations"""
    
    recommendations = []
    
    # Overall score recommendations
    if overall_stats['avg_hrv_score'] < 0.6:
        recommendations.append("Focus on improving overall content quality through better HRV balance")
    
    # Dimension-specific recommendations
    for dimension, stats in dimension_stats.items():
        if stats['mean'] < 0.4:
            recommendations.append(f"Improve {dimension.replace('_', ' ')} across the corpus")
        elif stats['std'] > 0.3:
            recommendations.append(f"Standardize {dimension.replace('_', ' ')} for consistency")
    
    # Diversity recommendations
    if overall_stats['hrv_score_std'] < 0.1:
        recommendations.append("Increase content diversity by varying HRV characteristics")
                    

Strategic Recommendations

🎬

Improve emotional valence across the corpus

📈

Standardize curiosity index for consistency

🖌️

Increase content diversity by varying HRV characteristics

📈

Focus on improving overall content quality through HRV balance

⚡

Enhance engagement elements in low-performing documents

🖌️

Add more storytelling and metaphor elements

Technical Implementation Thesis

The corpus_analysis.py module represents the advanced data science capabilities of ResonanceOS v6, demonstrating how the system can be leveraged for comprehensive text corpus analysis, pattern recognition, and quality assessment at scale. This implementation showcases sophisticated understanding of statistical analysis, HRV pattern recognition, quality metrics, and data-driven recommendations while providing practical tools for researchers, content managers, and data scientists to understand and optimize large text collections using human resonance metrics and advanced analytics.

Data Science Philosophy

Statistical Rigor: Comprehensive statistical analysis and pattern recognition
HRV Metrics: Multi-dimensional quality assessment using HRV vectors
Scalable Analysis: Efficient batch processing of large document collections
Actionable Insights: Data-driven recommendations for improvement

Key Analytical Features

Single Document Analysis

Comprehensive HRV and quality assessment for individual documents.

Batch Processing

Efficient analysis of large document collections with aggregate insights.

Quality Classification

Multi-tier quality assessment with scoring and recommendations.

Style Analysis

Writing style identification and characteristic analysis.