TweetMyDisasters
An NLP-based platform that categorizes disaster-related tweets and provides actionable insights for response teams to improve emergency preparedness and response.
Key Features
- Real-time tweet categorization
- Sentiment analysis of tweets
- Disaster type identification using NLP
- Actionable insights for response teams
Tech Stack
Python, TensorFlow, MongoDB, Twitter API
TweetMyDisasters - Analyzing Disaster Tweets in Real-Time
TweetMyDisasters uses NLP to analyze and categorize tweets related to natural disasters, enabling response teams to gain real-time insights on the ground situation. The platform helps prioritize emergency actions based on tweet sentiment and disaster types, improving the efficiency of disaster response.
Key Problem
Developing a robust NLP model that can accurately categorize and analyze disaster-related tweets in real-time, while filtering out irrelevant content, was a key challenge. Ensuring the system could handle large volumes of data from Twitter was essential.
Project Highlights
- ✔️ Real-time disaster tweet categorization
- ✔️ Sentiment analysis for prioritizing responses
- ✔️ Scalable to handle high tweet volumes