VedasteTuyishimire/Rwanda-Transport-Fare-Sentiment-Analysis-Dashboard
Domain:
natural language processingsocioeconomicRecord type:
project
Creator:
Ved
Host:
# 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 …