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http://dx.doi.org/10.9708/jksci.2021.26.12.001

A Deep Learning Model for Disaster Alerts Classification  

Park, Soonwook (School of Software, Soongsil University)
Jun, Hyeyoon (School of Software, Soongsil University)
Kim, Yoonsoo (School of Software, Soongsil University)
Lee, Soowon (School of Software, Soongsil University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.12, 2021 , pp. 1-9 More about this Journal
Abstract
Disaster alerts are text messages sent by government to people in the area in the event of a disaster. Since the number of disaster alerts has increased, the number of people who block disaster alerts is increasing as many unnecessary disaster alerts are being received. To solve this problem, this study proposes a deep learning model that automatically classifies disaster alerts by disaster type, and allows only necessary disaster alerts to be received according to the recipient. The proposed model embeds disaster alerts via KoBERT and classifies them by disaster type with LSTM. As a result of classifying disaster alerts using 3 combinations of parts of speech: [Noun], [Noun + Adjective + Verb] and [All parts], and 4 classification models: Proposed model, Keyword classification, Word2Vec + 1D-CNN and KoBERT + FFNN, the proposed model achieved the highest performance with 0.988954 accuracy.
Keywords
Disaster Alerts; Text Classification; Deep Learning; Word Embedding; BERT;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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