Modern Sentiment Analysis Web App

A beautiful, modern web application for sentiment analysis with improved UI/UX design, built with Flask and featuring a robust fallback sentiment analysis system.

Features

• 🎨 Modern UI/UX Design: Clean, responsive interface with smooth animations
• 🧠 Dual Analysis System: Primary Skills Network Watson service with intelligent fallback
• 📱 Responsive Design: Works perfectly on desktop, tablet, and mobile devices
• ⚡ Real-time Feedback: Instant character counting and input validation
• 🎯 Visual Results: Confidence scores with animated progress bars for positive, negative, and neutral sentiment
• 🔄 Loading States: Smooth loading animations and user feedback
• 📝 Sample Texts: Pre-made examples to test the application quickly
• 🛡️ Robust Error Handling: Automatic fallback to local sentiment analysis when external service is unavailable
• 🎭 Three Sentiment Types: Positive, Negative, and Neutral with color-coded results

Screenshots

The app features:

• Gradient background with a modern card-based layout
• Animated buttons and interactive elements
• Color-coded sentiment results (positive/negative)
• Animated confidence score bars
• Character counting with color indicators
• Sample text buttons for quick testing

Installation

1. Clone the repository:

git clone <your-repo-url>
cd sentiment-analysis

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. No additional setup required!
   • The application uses a fallback sentiment analysis system that works without external API keys
   • Primary service: Skills Network Watson (automatic fallback if unavailable)
   • Secondary service: Local rule-based sentiment analysis (always available)

Usage

1. Run the application:

python app.py

2. Open your browser and navigate to:

http://localhost:5000

3. Use the application:
   • Enter text in the textarea
   • Click "Analyze Sentiment" button
   • View the results with confidence scores
   • Try sample texts for quick testing

Technical Details

Backend

• Flask: Lightweight web framework for Python
• Skills Network Watson: Primary sentiment analysis service
• Local Fallback: Rule-based sentiment analysis for reliability
• JSON Processing: Efficient data handling and parsing
• Error Handling: Robust exception management with automatic fallback

Frontend

• HTML5: Modern semantic markup
• CSS3: Advanced styling with gradients, animations, and flexbox
• JavaScript: Modern ES6+ features with async/await
• Font Awesome: Icons for better visual appeal
• Google Fonts: Inter font family for modern typography

Features Implemented

• Character counting with color coding
• Auto-resizing textarea
• Loading states and animations
• Error handling with user-friendly messages
• Responsive design for all screen sizes
• Sample text functionality
• Keyboard shortcuts (Ctrl+Enter to analyze)
• Celebration animations for high confidence results
• Three-tier sentiment classification (Positive, Negative, Neutral)
• Automatic service fallback for maximum reliability

API Endpoints

• GET /: Main application page
• POST /analyze: Sentiment analysis endpoint
  • Input: textToAnalyze (form data)
  • Output: JSON with label and score

Customization

Styling

Modify static/style.css to change:

• Color schemes
• Typography
• Layout and spacing
• Animations and transitions

Functionality

Modify static/script.js to add:

• New sample texts
• Additional validation
• Enhanced animations
• New features

Backend

Modify app.py to:

• Use different models
• Add new endpoints
• Implement additional features

Model Information

Primary Service: Skills Network Watson

• Service: Skills Network Watson Sentiment Analysis
• Type: BERT-based sentiment analysis model
• Labels: positive, negative, neutral
• Confidence: Score between 0 and 1 (confidence level)

Fallback Service: Local Rule-Based Analysis

• Type: Dictionary-based sentiment analysis
• Method: Positive/negative word counting with weighted scoring
• Labels: POSITIVE, NEGATIVE, NEUTRAL
• Confidence: Calculated based on word frequency and sentiment strength
• Reliability: Always available, no network dependency

Browser Support

• Chrome (recommended)
• Firefox
• Safari
• Edge
• Mobile browsers

Performance Notes

• Primary Service: Fast API responses from Skills Network Watson service
• Fallback Service: Instant local analysis with no network dependency
• Reliability: 100% uptime with automatic fallback system
• Accuracy: High accuracy from BERT-based primary model, reasonable accuracy from rule-based fallback
• No API Limits: Free to use with no usage restrictions

Project Structure

sentiment-analysis/
├── app.py                    # Main Flask application
├── sentiment_analysis.py     # Sentiment analysis logic with fallback
├── test_sentiment.py        # Test script for validation
├── requirements.txt         # Python dependencies
├── README.md               # This file
├── DEPLOYMENT.md           # Deployment guide
├── QUICK_START.md          # Quick start guide
├── Procfile                # Deployment configuration
├── runtime.txt             # Python version specification
├── .env.example            # Environment variables template
├── .gitignore              # Git ignore file
├── templates/
│   └── index.html          # Main HTML template
└── static/
    ├── style.css           # CSS styling
    └── script.js           # JavaScript functionality

Testing

Run the included test script to verify functionality:

python test_sentiment.py

This will test various text inputs and show the sentiment analysis results.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is open source and available under the MIT License.

Deployment Options

See the DEPLOYMENT.md file for detailed instructions on hosting this application for free on various platforms.

Acknowledgments

• Skills Network for the Watson sentiment analysis service
• Flask community for the excellent web framework
• Font Awesome for beautiful icons
• Google Fonts for modern typography
• Python community for the amazing ecosystem