Process multiple social media posts

Analyze sentiment for a single text

Comprehensive sentiment analysis for hazard-related content Returns: { 'polarity': float, # -1 to 1 'subject

Enhanced hazard classification with confidence scoring Returns: (hazard_type, urgency_level, confidence_score)

Enhanced classification with confidence scoring

Process a single social media post through the complete pipeline

Run a command and handle errors

Apply filters to base report count

Calculate confidence score for the sentiment analysis

Calculate urgency score based on emergency keywords and patterns

Create empty result for posts with no processable text

Return default sentiment for empty text

Determine sentiment label considering both polarity and urgency

Return empty summary for no data

Extract emergency-related indicators from text

Generate daily trends data

Generate geographic distribution data

Generate hazard type distribution

Generate hourly trends data

Generate location distribution

Generate ML insights data

Generate monthly trends data

Generate response metrics data

Generate sentiment analysis data

Generate social media insights data

Generate source distribution

Generate weekly trends data

Analyze sentiment for multiple texts

Check if Python version is compatible

Create necessary data directories

Download required NLTK data

Extract potential location mentions from text

Generate comprehensive analytics data

Generate summary statistics for processed posts

Get high priority posts (high urgency + high confidence)

Install Python requirements

Install spaCy English model

Main test function

Main setup function