Natural Language Processing - Neural Network for analyzing disaster response data
Classify Your Messageto see how NLP model predict your request.
Classify Sample Messageto let the NLP model predict a real SOS message from the sample database.
You never know which will come first, tomorrow or the disaster. Once a disaster occurs, beyond the 72
golden hours, the
death rate increases dramatically.
However, how to race against time to better perform supplies estimation and job assigning?
Big Data and AI can help us accurately predict the amount of aid and supplies needed for victims, leading to better allocation of supplies and manpower by government agencies and relief organizations.
The database (credit appen) for this project is from the real world:
26,216 messages, 3 genres
(direct, news, and social), 36 categories.
The project was built on a deep learning neural network model, with 95% average Recall Scores for all of the categories.
Why Recall Score? It more focuses on quantity, whether or not irrelevant messages are also included. e.g., "I have food, I can help my neighborhood." maybe treat as an SOS message. However, SOS is so important that we should take the risk of classifying irrelevant as SOS.