README.md

metadata

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

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

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Status: ✅ Production Ready - Enterprise-level sentiment analysis with advanced NLP capabilities.