VedasteTuyishimire/Rwanda-Transport-Fare-Sentiment-Analysis-Dashboard

# Rwanda-Transport-Fare-Sentiment-Analysis-Dashboard ## Overview This project analyzes public sentiment regarding Rwanda's new distance-based fare system in public transport. It processes data from various sources to provide insights for policymakers through an interactive dashboard. ## Features - Multi-source data collection (Twitter, news comments, forums) - Sentiment analysis using state-of-the-art NLP models - Interactive dashboard with temporal and geographical visualizations - Trend analysis and key concerns identification - Automated misinformation detection ## Tech Stack - **Data Collection**: Tweepy, Selenium, BeautifulSoup4 - **Data Processing**: Pandas, NumPy - **NLP & ML**: Transformers (BERT), spaCy, scikit-learn - **Visualization**: Plotly, Dash - **Deployment**: Docker, FastAPI - **Database**: MongoDB ## Project Structure ``` ├── data/ # Data storage │ ├── raw/ # Raw collected data │ └── processed/ # Processed datasets ├── notebooks/ # Jupyter notebooks for analysis ├── src/ # Source code │ ├── collectors/ # Data collection scripts │ ├── processors/ # Data processing modules │ ├── models/ # ML models and training │ ├── dashboard/ # Dashboard application │ └── utils/ # Utility functions ├── tests/ # Unit tests ├── requirements.txt # Python dependencies └── docker/ # Docker configuration ``` ## Setup Instructions 1. Clone the repository ```bash git clone github.com cd rwanda-transport-sentiment ``` 2. Create and activate virtual environment ```bash python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ``` 3. Install dependencies ```bash pip install -r requirements.txt ``` 4. Set up environment variables ```bash cp .env.example .env # Edit .env with your API keys and configurations ``` 5. Run the dashboard ```bash python src/dashboard/app.py ``` ## Data Sources - Twitter API …