A Deep Learning Model for Disaster Alerts Classification (재난문자 분류를 위한 딥러닝 모델)

PARK SOONWOOK (박순욱); Hyeyoon Jun (전혜윤); Yoonsoo Kim (김윤수); Soowon Lee (이수원)

doi:10.9708/jksci.2021.26.12.001

A Deep Learning Model for Disaster Alerts Classification

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(12), pp.1-9
DOI : 10.9708/jksci.2021.26.12.001
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : June 23, 2021
Accepted : November 22, 2021
Published : December 31, 2021

PARK SOONWOOK ¹, Hyeyoon Jun ², Yoonsoo Kim ², Soowon Lee ²

¹숭실대학교 지능형로봇연구소
²숭실대학교

Accredited

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

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A Deep Learning Model for Disaster Alerts Classification

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

A Deep Learning Model for Disaster Alerts Classification

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.