Disaster Response NLP

TLDR

• Processed roughly 45K disaster-related tweets across 14 events to extract location, need, and urgency signals from messy real-time social media.

• Combined RoBERTa, VADER, word-frequency analysis, and geospatial/textual modeling to identify areas of highest need.

• Cut inference time roughly in half with batch processing, making full-dataset iteration practical.