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

• Published in: Procedia Computer Science
• Main Author: Ma'ady M.N.P.; Rahim A.F.A.; Syahda T.S.N.; Rizqi A.F.; Ratna M.C.A.
• Format: Conference paper
• Language: English
• Published: Elsevier B.V. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193203396&doi=10.1016%2fj.procs.2024.03.040&partnerID=40&md5=cc76fcc355eaaf211f5c50f8a7518206

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. © 2023 The Authors. Published by Elsevier B.V.