🏈 NFL Sentiment Analysis

A sophisticated machine learning project that performs real-time sentiment analysis on NFL-related social media content using the RoBERTa pre-trained model. The analysis provides valuable insights into public sentiment towards NFL teams during the active season.

📊 View Dataset on Kaggle 📝 Read the Article on Medium

🌟 Key Features

• Real-time social media data collection for NFL teams
• Context-aware sentiment analysis using RoBERTa
• Intelligent parsing of NFL-specific scenarios:
  • Draft pick implications
  • Injury impact assessment
  • Team performance metrics
• Automated data export and visualization
• Season-specific context optimization

📊 Examples

Model Context Intelligence

The model has been specifically trained to understand NFL-specific contexts:

• Negative sentiment for season-ending injuries
• Draft position implications during active season
• Team performance trends and playoff implications

🚀 Getting Started

Prerequisites

• Python 3.8+
• pip package manager
• Git

Installation

1. Clone the repository:

git clone https://github.com/cam-cc/NFL-sentiment-analysis.git
cd NFL-sentiment-analysis

2. Install dependencies:

pip install -r requirements.txt

💻 Usage

1. Configure your settings:

# create a .env
TWITTER_USERNAME=Username
TWITTER_PASSWORD=Password

2. Run the analysis: FROM ROOT DIR

python ./src/main.py

3. Test custom inputs:

python ./tests/test-roberta.py

Data will be exported to data/TEAM_TIMESTAMP.csv

📈 Model Testing

To test the sentiment analysis on custom text:

1. Open test-roberta.py
2. Modify the sample text
3. Run the script
4. View the comprehensive sentiment analysis output

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

✨ Acknowledgments

• RoBERTa model developers
• Twitter I guess

⚠️ Note

This model is optimized for in-season analysis. Performance may vary during the off-season due to different contextual indicators.