Deep Context Sentiment Analysis

A production-ready sentiment analysis system that processes social media comments with advanced contextual understanding. The system leverages Google's Gemini AI to analyze sentiment across multiple social media platforms and provides detailed justifications for each analysis.

Features

• Advanced contextual sentiment analysis of social media comments
• Support for multiple platforms (YouTube, Facebook, Twitter, Instagram)
• Powered by Google Gemini AI for accurate sentiment detection
• Detailed analysis logging and justification for each comment
• 6 distinct sentiment categories with context-aware classification
• Batch processing for efficient analysis of large comment volumes
• Modern "Dark Mode" Dashboard:
  • Glassmorphism design with neon accents
  • Interactive visualization including sentiment distribution charts
  • Smooth animations and transitions
  • Responsive layout for valid analysis results
• Comprehensive API with detailed documentation
• Docker containerization for easy deployment
• Structured logging and monitoring

Tech Stack

Backend

• FastAPI with async/await for high-performance API
• Google Gemini AI for contextual sentiment analysis
• httpx for async API calls to social platforms
• Pydantic v2 for robust data validation
• structlog for structured logging
• pytest with asyncio for async testing
• Apify integration for social media scraping

AI Features

• 6 distinct sentiment categories:
  • Supportive/Empathetic
  • Critical/Disapproving
  • Sarcastic/Ironic
  • Informative/Neutral
  • Appreciative/Praising
  • Angry/Hostile
• Context-aware analysis considering:
  • Post content and media
  • Platform-specific features
  • Comment timing and thread context
• Detailed justifications for each sentiment classification
• Batch processing with configurable sizes
• Parallel analysis with rate limiting

Logging System

• Advanced AI agent interaction logging with structured data
• JSON-based log files with automatic rotation
• Complete context tracking for each analysis session
• Detailed performance metrics and timing data
• Query-friendly log format for analytics
• Response validation and error tracking
• Session-based logging with unique identifiers
• Custom log levels for different analysis phases
• Real-time log aggregation and monitoring
• Historical trend analysis support

Getting Started

Prerequisites

• Docker and Docker Compose
• Python 3.11+ (for local development)
• Node.js 18+ (for frontend development)

Environment Setup

1. Clone the repository:

   git clone [repository-url]
   cd deep-context-sentiment-analysis

2. Set up a Python virtual environment:

   conda create -n sentiment-analysis python=3.12
   conda activate sentiment-analysis

3. Copy the example environment file:

   cp .env.example .env

4. Update the environment variables in .env:

   # API Keys
   GOOGLE_GEMINI_API_KEY=your_gemini_api_key
   APIFY_API_TOKEN=your_apify_token
   
   # App Configuration
   APP_NAME=Sentiment Analysis API
   VERSION=1.0.0
   DEBUG=True
   
   # Processing Settings
   BATCH_SIZE=10
   MAX_COMMENTS=100
   MAX_CONCURRENT_BATCHES=5

Running with Docker

1. Build and start the containers:

   docker compose up --build

2. Access the services:

   • Backend API: http://localhost:8000
   • Frontend Dashboard: http://localhost:3000
   • API Documentation: http://localhost:8000/docs

Local Development

1. Backend Setup:

   python -m venv venv
   source venv/bin/activate  # or `venv\Scripts\activate` on Windows
   pip install -r requirements.txt
   uvicorn app.main:app --reload

2. Frontend Setup:

   cd frontend
   npm install
   npm run dev

API Documentation

Endpoints

• POST /api/v1/analyze

  • Analyzes sentiment from a social media URL
  • Input: {"url": "https://platform.com/post"}
  • Returns detailed sentiment analysis with:
    • Overall sentiment summary
    • Per-comment analysis with justifications
    • Processing metrics and timing data

• GET /api/v1/analyze/demo

  • Returns sample analysis response
  • Useful for testing and development

• GET /api/v1/platforms

  • Lists supported platforms and their limits
  • Includes example URLs and rate limits

Example Request & Response

# Request
curl -X POST "http://localhost:8000/api/v1/analyze" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.youtube.com/watch?v=example"}'

# Response
{
  "status": "completed",
  "timestamp": "2024-01-14T12:00:00Z",
  "postUrl": "https://www.youtube.com/watch?v=example",
  "platform": "YOUTUBE",
  "summary": {
    "totalComments": 100,
    "supportive_empathetic": 25,
    "critical_disapproving": 15,
    "sarcastic_ironic": 20,
    "informative_neutral": 20,
    "appreciative_praising": 15,
    "angry_hostile": 5
  },
  "topComments": {
    "Supportive/Empathetic": ["Great explanation!", ...],
    "Critical/Disapproving": ["This could be better...", ...],
    ...
  },
  "processingTime": 8.5,
  "batchesProcessed": 10
}

## Testing

### Backend Tests

```bash
pytest tests/ --cov=app

Frontend Tests

cd frontend
npm test

Performance

Processing Times

• Initial Scraping: 2-5 seconds
• Batch Analysis (100 comments): 6-10 seconds
• Total Response Time: 8-16 seconds

Reliability Metrics

• Error Rate: <5%
• Batch Success Rate: >95%
• API Availability: >99.9%

System Capacity

• Concurrent Users: Up to 100
• Comments per Batch: 10-50 (configurable)
• Max Comments per Request: 1000

AI Performance

• Sentiment Accuracy: ~90%
• Context Understanding: High
• Language Support: 10+ languages
• Response Latency: 150-300ms per comment

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google Gemini AI for sentiment analysis
• Apify for social media scraping
• The FastAPI and React communities