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Sentiment Scanner

The Sentiment Scanner evaluates the emotional tone of text content using advanced Natural Language Processing (NLP) techniques. It leverages NLTK's SentimentIntensityAnalyzer to provide accurate sentiment analysis for content moderation and monitoring.

Sentiment Scanner Architecture

Sentiment analysis workflow using NLTK's VADER sentiment analyzer

How It Works

Using NLTK's VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analyzer, the scanner calculates sentiment scores ranging from -1 (extremely negative) to +1 (extremely positive), with 0 representing neutral sentiment.

Key Features

  • Comprehensive sentiment analysis
  • Configurable sentiment thresholds
  • Real-time content evaluation
  • Support for multiple languages
  • High accuracy sentiment detection

Sentiment Score Range

  • -1.0 to -0.5: Strong negative sentiment
  • -0.5 to -0.1: Moderate negative sentiment
  • -0.1 to 0.1: Neutral sentiment
  • 0.1 to 0.5: Moderate positive sentiment
  • 0.5 to 1.0: Strong positive sentiment

Configuration Options

  • Threshold: Minimum acceptable sentiment score (default: 0)
  • Custom Lexicons: Support for domain-specific sentiment vocabularies
  • Language Settings: Configuration for multi-language support

Output Format

The scanner returns a tuple containing:

  • sanitized_prompt: The analyzed text
  • is_valid: Boolean indicating if sentiment meets threshold
  • risk_score: Normalized negative sentiment score (0-1)

Note: Sentiment analysis accuracy may vary with context and language. Consider implementing additional context-aware rules for specific use cases.

Tip: For optimal results, consider adjusting the sentiment threshold based on your specific use case and content type. Regular monitoring and threshold adjustment can help improve accuracy.