# 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**:
   ```bash
   git clone https://github.com/your-username/advanced-sentiment-analysis.git
   cd advanced-sentiment-analysis
   ```

3. **Create a virtual environment**:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

4. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

5. **Set up environment variables**:
   ```bash
   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

```bash
# 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

```python
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](https://github.com/stanfordnlp/dspy)
- [OpenAI API Documentation](https://platform.openai.com/docs)
- [Python Testing Best Practices](https://docs.python.org/3/library/unittest.html)
- [Jupyter Notebook Best Practices](https://jupyter.org/community)

### 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

---

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