@article{M1D80B45A,
title = "Evaluating Urgency Levels of Emergency Alerts through Sentiment Analysis",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.2.226",
author = "Sang-Lim Ju, Hyunjoo Kang, Seung-Hee Oh",
keywords = "Emergency Alerts, Natural Language Processing, Sentiment Analysis, Urgency Level evaluation",
abstract = "Emergency alerts are crucial means for promptly and accurately conveying disaster information in urgent situations. However, as the type and frequency of emergency alerts delivered to the public increase, there has been a rise in public fatigue and a decline in trust. To evaluate the appropriateness of the emergency alert service, this study proposes models that evaluate urgency levels for emergency alerts based on sentiment analysis. The models are developed using four representative natural language processing algorithms.
Furthermore, criteria and methodology for evaluating urgency levels are presented. In the experimental results, among the four algorithms, the urgency level evaluation model based on the Bidirectional Encoder Representations from Transformers algorithm showed the best learning performance with an accuracy of 98%.
And through the four learned algorithm-based urgency level evaluation models, the urgency of the emergency alerts issued in 2022 was classified and evaluated."
}