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Text Analysis System Documentation

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Overview

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Components

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1. Emotion Model

• Uses RoBERTa-based model fine-tuned on emotion detection
• Provides primary and secondary emotions with intensity scores
• Normalizes emotions to a standard set for consistency

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2. Therapy Technique Model

• Zero-shot classification for therapy technique identification
• Supports common techniques like CBT, DBT, mindfulness
• Provides confidence scores for detected techniques

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3. Crisis Detection Model

• Specialized model for identifying crisis situations
• Five-level severity classification
• Real-time trigger detection and immediate action flags

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4. Semantic Analysis

• Rule-based system for extracting:
  • Key phrases and themes
  • Relationship context
  • Temporal information
  • Setting detection

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5. HIPAA Compliance Checker

• Pattern-based PHI detection
• Identifies sensitive information categories
• Flags content requiring redaction

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Enhancements

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Emotion Model

• New Emotion Categories: Added shame, guilt, envy, pride, and relief to the emotion detection capabilities.

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Therapy Technique Model

• Expanded Techniques: Recognizes additional therapy techniques including:
  • Narrative Therapy
  • Acceptance Commitment Therapy
  • Emotionally Focused Therapy
  • Interpersonal Therapy
  • Play Therapy

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Crisis Detection Model

• Nuanced Categories: Enhanced crisis detection with:
  • Severe Anxiety
  • Depression
  • Psychosis

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Usage

import { TextAnalysisService } from '../services/TextAnalysisService'
import { SecurityAuditService } from '../services/SecurityAuditService'

// Initialize services
const securityAudit = new SecurityAuditService()
const textAnalysis = new TextAnalysisService(securityAudit)
await textAnalysis.initialize()

// Analyze text
const result = await textAnalysis.analyzeText(
  'I feel much better after our CBT session today.',
  'user123',
)

// Access analysis results
console.log(result.emotions) // Emotional state
console.log(result.therapyTechniques) // Detected techniques
console.log(result.crisisIndicators) // Crisis assessment
console.log(result.semanticAnalysis) // Context and themes
console.log(result.hipaaCompliance) // PHI detection

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Security and Compliance

1. All operations are logged through SecurityAuditService
2. PHI detection prevents accidental data exposure
3. Crisis detection triggers immediate safety protocols
4. Data is processed with appropriate encryption

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Testing

• Unit tests for each model
• Integration tests for the TextAnalysisService
• HIPAA compliance validation tests
• Crisis detection accuracy tests
• EmotionModel tests now cover unknown labels, empty inputs, and multiple emotions.
• TextAnalysisService tests include scenarios for handling no emotions, multiple techniques, and crisis detection.

npm run test

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Future Enhancements

1. Multi-modal Analysis
   • Audio emotion detection
   • Video sentiment analysis
   • Non-verbal cue recognition
2. Advanced Features
   • Therapy progress tracking
   • Outcome prediction
   • Treatment recommendation
3. Performance Optimization
   • Model quantization
   • Batch processing
   • Caching strategies

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Dependencies

• @xenova/transformers: ^2.15.0
• TypeScript
• (for testing)

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Contributing

1. Follow TypeScript best practices
2. Maintain HIPAA compliance
3. Add appropriate tests
4. Update documentation
5. Consider performance implications

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Monitoring and Maintenance

• Performance monitoring
• Error tracking
• Usage analytics
• Model version control

1. Update ML models
2. Review security logs
3. Validate HIPAA compliance
4. Optimize performance