📰 News Perspective Meter

A comprehensive web application that helps users analyze their news consumption patterns and identify potential filter bubbles in their reading habits. The app uses AI-powered sentiment analysis and interactive visualizations to provide personalized insights about media consumption diversity.

🌟 Features

📊 Reading Analytics

• Real-time tracking of articles read from different political perspectives
• Interactive pie charts showing reading distribution across Left, Center, and Right sources
• Statistical insights with detailed breakdowns and percentages
• Historical tracking of reading patterns over time

🤖 AI-Powered Analysis

• Sentiment analysis using DistilBERT model for news articles
• Automatic categorization of news sources by political leaning
• Confidence scoring for sentiment analysis results
• Database storage of all analysis results for trend tracking

🎯 Filter Bubble Detection

• Personalized analysis of reading habits
• Filter bubble warnings when over 70% of reading comes from one political category
• Actionable recommendations for diversifying news consumption
• Progress tracking towards more balanced reading habits

📱 Modern User Experience

• Responsive design that works on desktop, tablet, and mobile
• Clean, modern interface with intuitive navigation
• Real-time updates as users interact with the application
• Loading states and error handling for smooth user experience

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• Modern web browser (Chrome, Firefox, Safari, Edge)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/news-perspective-meter.git
   cd news-perspective-meter

2. Create a virtual environment

   python -m venv venv

3. Activate the virtual environment

   Windows:

   venv\Scripts\activate

   macOS/Linux:

   source venv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Run the application

   python app.py

6. Open your browser Navigate to http://127.0.0.1:5000

🛠️ Technologies Used

Backend

• Flask - Web framework for Python
• SQLite - Lightweight database for storing reading history
• Transformers - Hugging Face library for AI sentiment analysis
• Requests - HTTP library for API calls
• DistilBERT - Pre-trained model for sentiment analysis

Frontend

• HTML5 - Semantic markup structure
• CSS3 - Modern styling with CSS Grid and Flexbox
• JavaScript (ES6+) - Interactive functionality
• Chart.js - Interactive data visualization
• Fetch API - Asynchronous HTTP requests

APIs

• NewsAPI - Real-time news headlines (optional)
• Custom REST API - Backend endpoints for data management

📁 Project Structure

news-perspective-meter/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── reading_history.db     # SQLite database (created automatically)
├── sources.json          # News sources configuration
├── templates/
│   └── index.html        # Main HTML template
├── static/
│   ├── style.css         # CSS styles
│   ├── script.js         # Original JavaScript
│   └── app.js           # Enhanced JavaScript functionality
└── README.md            # This file

🔧 Configuration

Environment Variables

Create a .env file in the root directory:

# Optional: NewsAPI key for real news data
NEWS_API_KEY=your_newsapi_key_here

# Flask configuration
FLASK_ENV=development
FLASK_DEBUG=True

News Sources

The application includes 45+ news sources across political categories:

• Left-leaning: The New York Times, CNN, The Guardian, etc.
• Center: BBC News, Reuters, NPR, Associated Press, etc.
• Right-leaning: Fox News, The Wall Street Journal, Breitbart, etc.

Sources are configured in sources.json and can be customized as needed.

📊 API Endpoints

News Endpoints

• GET / - Main application page
• GET /news - Fetch latest headlines (default source)
• GET /api/headlines/<source> - Fetch headlines from specific source

Analysis Endpoints

• POST /analyze-sentiment - Analyze article sentiment
• GET /analyze-sentiment/<text> - Analyze sentiment via GET

Data Endpoints

• GET /reading-history - Retrieve user's reading history
• GET /reading-statistics - Get reading pattern statistics
• POST /add-reading-record - Manually add reading record

🎨 Customization

Styling

The application uses CSS custom properties for easy theming. Modify the color palette in static/style.css:

:root {
    --primary-color: #2563eb;    /* Main brand color */
    --accent-color: #06b6d4;     /* Accent color */
    --left-color: #ef4444;       /* Left-leaning sources */
    --center-color: #06b6d4;     /* Center sources */
    --right-color: #3b82f6;      /* Right-leaning sources */
}

Adding News Sources

Edit sources.json to add new sources:

{
    "sourceName": "Your News Source",
    "category": "Left|Center|Right",
    "articlesRead": 0,
    "country": "International|India"
}

🔍 Usage Guide

1. Loading News

• Select a news source from the dropdown
• Click "Load Latest News" to fetch headlines
• Browse through the displayed articles

2. Analyzing Articles

• Click "Analyze Sentiment" on any headline
• View the AI-powered sentiment analysis
• The analysis is automatically saved to your reading history

3. Viewing Analytics

• The pie chart shows your reading distribution
• Statistics cards display counts for each category
• The analysis section provides personalized insights

4. Understanding Recommendations

• Filter Bubble Warning: Appears when >70% of reading comes from one category
• Balanced Reading: Praises diverse consumption patterns
• Actionable Tips: Specific suggestions for improving news diversity

🧪 Testing

Manual Testing

1. Load different news sources
2. Analyze various articles
3. Check that statistics update correctly
4. Verify responsive design on different screen sizes

API Testing

# Test sentiment analysis
curl -X POST http://127.0.0.1:5000/analyze-sentiment \
  -H "Content-Type: application/json" \
  -d '{"text": "This is a test article", "store_record": true}'

# Test reading statistics
curl http://127.0.0.1:5000/reading-statistics

🚀 Deployment

Local Development

python app.py

Production Deployment

1. Set FLASK_ENV=production
2. Use a production WSGI server (e.g., Gunicorn)
3. Configure reverse proxy (e.g., Nginx)
4. Set up SSL certificates
5. Configure environment variables

Docker Deployment

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 5000
CMD ["python", "app.py"]

🤝 Contributing

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

📝 License

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

🙏 Acknowledgments

• Hugging Face for the DistilBERT sentiment analysis model
• Chart.js for interactive data visualization
• NewsAPI for news data (when API key is provided)
• Flask community for the excellent web framework

📞 Support

For support, email support@newsperspectivemeter.com or create an issue in the GitHub repository.

🔮 Future Enhancements

• User authentication and profiles
• Social sharing of reading insights
• Advanced analytics and reporting
• Mobile app development
• Integration with more news APIs
• Machine learning for better categorization
• Export functionality for reading reports

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Made with ❤️ for promoting media literacy and diverse news consumption