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| title: Advanced Sentiment Analytics Dashboard | |
| emoji: 📊 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| pinned: false | |
| license: mit | |
| app_port: 7860 | |
| # 🚀 Advanced Sentiment Analytics Dashboard | |
| A distributed sentiment analysis system with **PyABSA + HF Spaces backend** and **Streamlit Cloud frontend** for scalable, high-accuracy analysis of product reviews. | |
| ## 🏗️ Architecture | |
| ### 🔧 Split Deployment Strategy | |
| - **Backend**: HuggingFace Spaces (PyABSA + FastAPI) - `api_server.py` | |
| - **Frontend**: Streamlit Cloud (Lightweight UI) - `frontend_light.py` | |
| - **Benefits**: High accuracy models + fast, scalable frontend | |
| ## ✨ Features | |
| ### 🔍 Core Analytics | |
| - **PyABSA Integration**: State-of-the-art aspect-based sentiment analysis | |
| - **M2M100 Translation**: Facebook's multilingual translation model | |
| - **Intent Classification**: Advanced customer intent detection | |
| - **Real-time Processing**: API-based architecture for scalability | |
| ### 📊 Advanced Dashboard | |
| - **Multi-page Navigation**: Home, Analytics, History, Documentation | |
| - **Interactive Visualizations**: Network graphs, heatmaps, timelines | |
| - **Advanced Filtering**: Multi-dimensional data exploration | |
| - **Session Management**: Save and restore analysis sessions | |
| - **KPI Dashboard**: Real-time metrics and insights | |
| ### 🎯 Business Intelligence | |
| - **Areas of Improvement**: AI-powered identification of problem areas | |
| - **Strength Anchors**: Recognition of positive aspects to leverage | |
| - **Alert System**: Automated sentiment spike detection | |
| - **Impact Simulation**: What-if analysis for business decisions | |
| - **Export Functionality**: PDF reports and Excel data export | |
| ## 📝 Data Format | |
| Your CSV file should include these columns: | |
| - `id`: Unique identifier for each review | |
| - `reviews_title`: Title of the review | |
| - `review`: The actual review text | |
| - `date`: Review date (YYYY-MM-DD format) | |
| - `user_id`: Identifier for the reviewer | |
| ## 🚀 Usage | |
| ### For End Users: | |
| 1. **Upload your CSV file** with review data (see format below) | |
| 2. **Process the data** using our advanced NLP pipeline (~2-3 minutes for 1000 reviews) | |
| 3. **Explore insights** through interactive visualizations | |
| 4. **Export results** as PDF reports or Excel files | |
| ### For Developers: | |
| #### Docker Deployment (Hugging Face Spaces) | |
| This app is optimized for Docker deployment with: | |
| - `Dockerfile`: Production-ready container setup | |
| - `requirements-docker.txt`: Optimized dependencies with version pinning | |
| - `.dockerignore`: Efficient build context | |
| - Health checks and proper port configuration (7860) | |
| #### Local Development | |
| ```bash | |
| git clone <your-repo> | |
| cd insights | |
| pip install -r requirements.txt | |
| streamlit run app_enhanced.py | |
| ``` | |
| ## 🛠️ Technology Stack | |
| - **Frontend**: Streamlit with interactive components | |
| - **NLP**: pyABSA for aspect-based sentiment analysis | |
| - **Translation**: Facebook M2M100 for multilingual support | |
| - **Visualization**: Plotly for interactive charts and graphs | |
| - **Network Analysis**: NetworkX for aspect relationship graphs | |
| ## 📊 Sample Output | |
| The dashboard provides: | |
| - Comprehensive sentiment analysis | |
| - Aspect extraction and sentiment mapping | |
| - Intent classification with confidence scores | |
| - Interactive network graphs of aspect relationships | |
| - Time-series analysis of sentiment trends | |
| - Exportable business intelligence reports | |
| --- | |
| **Status**: ✅ **Production Ready** - Enterprise-level sentiment analysis with advanced NLP capabilities. |