CONTRIBUTING.md

Contributing to Advanced Sentiment Analysis System

Thank you for your interest in contributing to the Advanced Sentiment Analysis System! This document provides guidelines and information for contributors.

🌟 Getting Started

Prerequisites

• Python 3.8 or higher
• OpenAI API key for testing
• Git for version control
• Jupyter Notebook for development

Development Environment Setup

1. Fork the repository on GitHub

2. Clone your fork locally:

   git clone https://github.com/your-username/advanced-sentiment-analysis.git
   cd advanced-sentiment-analysis
   

3. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

4. Install dependencies:

   pip install -r requirements.txt
   

5. Set up environment variables:

   cp .env.example .env
   # Edit .env with your OpenAI API key
   

🔧 Development Guidelines

Code Style

We follow Python best practices and maintain consistent code style:

• PEP 8 compliance for Python code
• Type hints for function parameters and return values
• Docstrings for all classes and functions (Google style)
• Meaningful variable names and clear code structure

Testing

• Unit tests for individual components
• Integration tests for system workflows
• Performance tests for scalability validation
• Example-based tests with real-world scenarios

Documentation

• Inline comments for complex logic
• Jupyter notebook documentation for tutorials and examples
• README updates for new features
• API documentation for public interfaces

🚀 How to Contribute

1. Issue Reporting

Before creating a new issue, please:

• Search existing issues to avoid duplicates
• Provide detailed information including:
  • System environment (Python version, OS)
  • Steps to reproduce
  • Expected vs. actual behavior
  • Error messages and logs

2. Feature Requests

When proposing new features:

• Describe the use case and problem being solved
• Explain the proposed solution with examples
• Consider backward compatibility and performance impact
• Discuss implementation approach if you have ideas

3. Pull Requests

Before submitting:

1. Create a feature branch from main
2. Implement your changes following coding guidelines
3. Add tests for new functionality
4. Update documentation as needed
5. Ensure all tests pass locally

Pull Request Process:

1. Create a clear title describing the change
2. Fill out the PR template (if available)
3. Link relevant issues using keywords (fixes #123)
4. Request review from maintainers
5. Address feedback promptly and professionally

🧪 Testing

Running Tests

# Install test dependencies
pip install pytest pytest-cov

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=. --cov-report=html

# Run specific test categories
pytest tests/ -m "unit"
pytest tests/ -m "integration"

Test Structure

tests/
├── unit/           # Unit tests for individual components
├── integration/    # Integration tests for workflows
├── performance/    # Performance and load tests
└── fixtures/       # Test data and utilities

📝 Documentation Standards

Jupyter Notebooks

• Clear cell organization with proper headings
• Executable examples with expected outputs
• Error handling demonstrations
• Performance considerations and optimization tips

Code Documentation

class SentimentAnalyzer:
    """
    Advanced sentiment analysis with multi-dimensional classification.
    
    This class provides comprehensive sentiment analysis including:
    - Primary sentiment classification (positive/negative/neutral)
    - Emotion detection (joy, anger, fear, etc.)
    - Aspect-based sentiment analysis
    - Confidence calibration and uncertainty quantification
    
    Example:
        >>> analyzer = SentimentAnalyzer()
        >>> result = analyzer.analyze("Great product!")
        >>> print(result.primary_sentiment)
        'positive'
    """

🌐 Community Guidelines

Code of Conduct

• Be respectful and professional in all interactions
• Welcome newcomers and help them get started
• Focus on constructive feedback in code reviews
• Acknowledge contributions and give credit where due

Communication

• Use clear, concise language in issues and PRs
• Provide context for your changes and decisions
• Ask questions when you need clarification
• Share knowledge and help others learn

🏆 Recognition

Contributors are recognized in several ways:

• Contributors list in README.md
• Release notes mention significant contributions
• GitHub contributors graph tracks all contributions
• Special recognition for major features or fixes

📋 Contribution Checklist

Before submitting your contribution:

• Code follows project style guidelines
• All tests pass locally
• New features include appropriate tests
• Documentation is updated for changes
• Commit messages are clear and descriptive
• No sensitive data (API keys, credentials) included
• Performance impact considered and documented

🔗 Resources

Useful Links

• DSPy Documentation
• OpenAI API Documentation
• Python Testing Best Practices
• Jupyter Notebook Best Practices

Getting Help

• GitHub Issues: For bug reports and feature requests
• GitHub Discussions: For questions and general discussion
• Documentation: Check README.md and inline documentation first

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Thank you for contributing to the Advanced Sentiment Analysis System! Your contributions help make this project better for everyone. 🚀