{"id":23038905,"url":"https://github.com/abhy-kumar/nlpulse","last_synced_at":"2026-04-24T16:35:27.860Z","repository":{"id":258228131,"uuid":"869950522","full_name":"abhy-kumar/NLPulse","owner":"abhy-kumar","description":"This program aims to give a sentiment score to each headline featured in today's top stories. 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The system employs multiple sentiment analysis models, including VADER, FinBERT, and RoBERTa, to provide nuanced sentiment scoring.\n\n## 🌟 Key Features\n- **Multi-Model Sentiment Analysis**: Combines VADER, FinBERT, and RoBERTa models for robust sentiment scoring\n- **Real-time RSS Feed Processing**: Automatically fetches and analyzes news headlines\n- **Interactive Visualizations**: Comprehensive dashboards using Plotly\n- **Efficient Data Storage**: SQLite database with optimized indexing\n- **Duplicate Detection**: Intelligent similarity-based duplicate removal\n- **Comprehensive Analysis**: Including timeline views, sentiment distributions, and statistical breakdowns\n\n### Custom Configuration\n```python\nfrom news_analysis import DatabaseManager, SentimentAnalyzer, DataVisualizer\n\n# Initialize components\ndb = DatabaseManager('custom_database.db')\nanalyzer = SentimentAnalyzer()\nvisualizer = DataVisualizer()\n\n# Run specific analyses\nvisualizer.create_visualizations('custom_database.db')\n```\n\n## 📊 Visualization Types\n\n### Main Dashboard\n- Daily Entry Counts\n- Hourly Distribution\n- Sentiment Timeline\n- Summary Length Distribution\n- Sentiment Distribution\n- Weekly Patterns\n- Sentiment Moving Average\n- Headline Length vs Sentiment\n- Time of Day Sentiment\n\n### Headlines Analysis\n- Recent Headlines Table\n- Most Positive Headlines\n- Most Negative Headlines\n- Statistical Summaries\n\n## 🗄️ Database Schema\n\n### sentiment_scores Table\n```sql\nCREATE TABLE sentiment_scores (\n    date TEXT,\n    time TEXT,\n    title TEXT,\n    summary TEXT,\n    score REAL\n)\n```\n\n### Indexes\n- `idx_date`: Optimizes date-based queries\n- `idx_title`: Facilitates headline searches\n- `idx_score`: Improves sentiment-based filtering\n\n## 🔍 Duplicate Detection\n\nThe project includes functions to:\n\n- Eliminate duplicate or near-duplicate entries based on a similarity threshold.\n- Provide analysis and cleanup of the dataset for better performance and accuracy.\n\n### Configuration\n```python\n# Adjust similarity threshold (default: 0.85)\nremove_duplicates(db_path='news_sentiment.db', similarity_threshold=0.90)\n```\n\n## 📈 Performance Optimization\n\n### Database Optimization\n- Write-Ahead Logging (WAL) mode\n- Optimized cache settings\n- Efficient indexing strategy\n- Regular VACUUM operations\n\n### Processing Optimization\n- Thread pooling for parallel sentiment analysis\n- LRU caching for frequently accessed data\n- Batch processing capabilities\n- GPU acceleration when available\n\n## 📝 Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.\n\n### Guidelines\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- NLTK team for VADER sentiment analysis\n- Hugging Face for transformer models\n- Plotly team for visualization capabilities\n- Contributors and maintainers of all dependent libraries\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhy-kumar%2Fnlpulse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhy-kumar%2Fnlpulse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhy-kumar%2Fnlpulse/lists"}