Citation

Izumi S, Hori T, Yamane T, Chun P, Fujimori Y, Moriwaki R. Intel. Inform. Infrastruct. 2020; 1(J1): 398-405.

Copyright

(Copyright © 2020, Japan Society of Civil Engineers)

DOI

10.11532/jsceiii.1.J1_398

PMID

unavailable

Abstract

Posting to social networking services during a disaster includes information that is useful for rescue and evacuation, but it is still underutilized in information gathering. In this study, we constructed a deep learning model to determine whether the posts containing keywords related to the disaster are valid or not. In addition, we visualized the words that the model focuses on. The mapping was made possible by extracting the location information from the post. It is shown that the built Deep Learning model can classify the submissions with high accuracy. The mapping was shown that the location information was generally extracted correctly. This suggests its effectiveness in classifying posts during disasters.

Language: en

Keywords

deep learning; disaster prevension; microblog; natural language processing; text minig