Real-Time Tweet Sentiment Analysis Pipeline

Addresses the challenge of understanding public sentiment at massive scale: traditional batch analysis is too slow to capture trending opinions, and manual monitoring is impossible. Built a real-time system that continuously ingests high-volume social media streams and performs instant sentiment classification, enabling organizations to detect shifts in public perception before they trend. The core ML problem is balancing accuracy with latency—using advanced transformer models while maintaining sub-200ms response times even during traffic spikes. The system identifies anomalies in sentiment patterns, alerting stakeholders to unexpected reputation shifts, competitive threats, or viral opportunities. Enables data-driven decision making at the speed of social media, crucial for brands, campaigns, and research institutions that need to stay ahead of public discourse.