🎭 Real-Time Sentiment Analysis Dashboard

A production-ready sentiment analysis application powered by DistilBERT that analyzes text sentiment in real-time with an interactive dashboard.

🌟 Features

• Real-Time Analysis: Instant sentiment prediction using state-of-the-art AI
• Batch Processing: Upload CSV files with thousands of texts
• Interactive Dashboard: Beautiful visualizations with Plotly
• Historical Data: SQLite database stores all analyses
• Export Functionality: Download results as CSV
• Production Ready: Error handling, logging, and validation

🎯 Use Cases

• Customer Feedback Analysis: Analyze reviews and support tickets
• Social Media Monitoring: Track brand sentiment on Twitter/Reddit
• Market Research: Understand customer opinions at scale
• Content Moderation: Flag negative content automatically
• Academic Research: Study sentiment patterns in text data

🏗️ Architecture

┌─────────────────┐
│   Streamlit UI  │  ← Beautiful web interface
└────────┬────────┘
         │
┌────────▼────────┐
│ SentimentAnalyzer│  ← DistilBERT model (66M params)
└────────┬────────┘
         │
┌────────▼────────┐
│  Data Handler   │  ← SQLite database operations
└────────┬────────┘
         │
┌────────▼────────┐
│    Database     │  ← Stores all results & analytics
└─────────────────┘

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• 8GB RAM (minimum)
• 2GB free disk space

Installation

1. Clone the repository

git clone https://github.com/Emart29/sentiment-analysis-dashboard.git
cd sentiment-analysis-dashboard

2. Create virtual environment

python -m venv venv

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

This will download the DistilBERT model (~250MB) automatically on first run.

Running the Application

streamlit run app.py

The dashboard will open in your browser at http://localhost:8501

📖 Usage Guide

1. Single Text Analysis

Navigate to "Single Text Analysis" and:

• Enter or paste your text
• Click "Analyze Sentiment"
• View results with confidence scores

2. Batch Analysis

Two options available:

Option A: CSV Upload

• Prepare CSV with a column named text, comment, review, or message
• Upload via "Batch Analysis" → "Upload CSV"
• Click "Analyze All"

Option B: Manual Entry

• Go to "Batch Analysis" → "Manual Entry"
• Enter texts (one per line)
• Click "Analyze All"

3. Analytics Dashboard

View comprehensive analytics:

• Sentiment distribution (pie chart)
• Confidence score histogram
• Daily trends (line chart)
• Recent analyses table
• Export data to CSV

🧠 Model Details

Model: distilbert-base-uncased-finetuned-sst-2-english

• Architecture: DistilBERT (distilled BERT)
• Parameters: 66 million
• Training Data: Stanford Sentiment Treebank (SST-2)
• Accuracy: ~91% on SST-2 test set
• Speed: 60% faster than BERT
• Memory: Only 255MB

Why DistilBERT?

• ✅ Lightweight enough for laptops
• ✅ High accuracy (97% of BERT's performance)
• ✅ Fast inference (~100ms per text)
• ✅ Well-maintained by Hugging Face
• ✅ Perfect for production use

📊 Project Structure

sentiment-analysis-dashboard/
│
├── app.py                    # Main Streamlit application
├── sentiment_analyzer.py     # DistilBERT sentiment analysis
├── data_handler.py          # SQLite database operations
├── utils.py                 # Helper functions & visualizations
├── requirements.txt         # Python dependencies
├── README.md               # This file
├── .gitignore              # Git ignore rules
│
├── data/                   # Database storage
     └── sentiment.db        # SQLite database (auto-created)

🛠️ Technical Stack

Component	Technology	Purpose
ML Model	DistilBERT	Sentiment classification
ML Framework	Transformers	Model loading & inference
Backend	PyTorch	Neural network operations
Frontend	Streamlit	Web dashboard
Database	SQLite	Data persistence
Visualization	Plotly	Interactive charts
Data Processing	Pandas	Data manipulation

⚡ Performance

Tested on Dell Latitude 5400 (i5-8th gen, 8GB RAM):

• Single text analysis: ~150ms
• Batch (100 texts): ~8 seconds
• Memory usage: ~600MB
• Database operations: <10ms per query

🔮 Future Enhancements

• Multi-class sentiment (Very Negative, Negative, Neutral, Positive, Very Positive)
• Real-time Twitter/Reddit API integration
• Sentiment trend alerts via email
• Multi-language support
• User authentication
• RESTful API endpoint
• Docker containerization
• Cloud deployment (AWS/GCP/Azure)

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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.

👤 Author

Your Name

• GitHub: Emart29
• LinkedIn: Emmanuel Nwanguma

🙏 Acknowledgments

• Hugging Face for the Transformers library
• Streamlit for the amazing framework
• Stanford NLP for the SST-2 dataset

📞 Support

If you have any questions or run into issues, please:

1. Check the Issues page
2. Create a new issue with detailed description
3. Contact me directly via email or LinkedIn

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