NLP Global Analytics Platform
Large-scale sentiment analysis for 4M+ Ukrainian crisis tweets
Analyzing 4 million tweets requires robust infrastructure and intelligent NLP pipelines. I built an end-to-end system with Flask API, MongoDB sharding, and custom transformers for multi-language sentiment analysis.
The Challenge
Processing millions of social media posts at scale requires careful system design. Traditional relational databases couldn't handle this scale cost-effectively. The main challenges were handling 4M+ tweets with metadata, supporting multiple languages (English, Spanish, Ukrainian, Polish), and ensuring sub-second response times for the dashboard.
Volume
4M+ tweets with metadata, images, and engagement metrics.
Variety
Support for English, Spanish, Ukrainian, Polish, and more.
Velocity
Dashboard needs sub-second response times for real-time queries.
Storage
MongoDB sharding to handle massive document collections.
Technical Architecture
Pipeline Flow
- Data Ingestion: Twitter API streaming to preprocessing pipeline.
- NLP Processing: NLTK & Hugging Face Transformers for sentiment/entity extraction.
- Storage Layer: MongoDB sharded cluster handling 4M+ documents.
- API Layer: Flask REST API with caching for high-performance queries.
- Visualization: React dashboard with geospatial maps and D3 charts.
Research & Support Focus
"Supporting a research project meant different challenges: creating comprehensive API documentation for other researchers, building data export tools for statistical analysis in Python/R, designing error handling for malformed tweets, and providing troubleshooting guides for MongoDB replication lag. The thesis achieved 9.5/10 and is now used as reference material."