Malaysia Citizen Sentiment on Government Response Towards Covid-19 Disaster Management: Using LDA-based Topic Visualization on Twitter

This paper studies lessons learned from Covid-19 disaster management in Malaysia using machine learning techniques. First, we crawl Twitter data related to ‘covid’ with geo-location bounding-box. Then we contribute to propose LDA topics generated on citizen perception containing negative sentiment t...

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Bibliographic Details
Published in:Procedia computer science 2024, Vol.234, p.561-569
Main Authors: Ma'ady, Mochamad Nizar Palefi, Rahim, Ainatul Fathiyah Abdul, Syahda, Tabina Shafa Nabila, Rizqi, Annisa Fairuz, Ratna, Maharani Citra Adi
Format: Article
Language:English
Subjects:
latent dirichlet allocation
malaysia citizen perception
post-covid-19
the government of malaysia
topic visualization
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Summary:This paper studies lessons learned from Covid-19 disaster management in Malaysia using machine learning techniques. First, we crawl Twitter data related to ‘covid’ with geo-location bounding-box. Then we contribute to propose LDA topics generated on citizen perception containing negative sentiment towards government response; hence, we represent the data using VOSviewer and D3.js to emphasize topic modeling with respect to timestamp due to pattern analysis. As results, LDA-based topic visualization may recognize the accounts’ pattern that are assumed as the pillars of disaster management in Malaysia. This study gains insights from political science field. Implications of the results are also discussed.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.03.040