ANALISIS RESPON PUBLIK DAN PERMODELAN TOPIK MENGGUNAKAN LATENT DIRICHLET ALLOCATION METHOD (LDA) PADA BENCANA BANJIR BANDANG DI SUMATERA BARAT 2024 MELALUI TWITTER - Anggara, Yonatan Yolius; Arif, Nursida (Disaster Research Center, Universitas Sebelas Maret, Surakarta, Indonesia, 2025)
This study examines public sentiment during flash floods in West Sumatra by analyzing Twitter data using NLP through text2data.com. It employs the Latent Dirichlet Allocation (LDA) method for topic modeling to identify key discussion themes. The results reveal that 97.9% of expressed sentiments were positive, focusing on disaster impacts, situational conditions, causes of floods, and public responses to government actions in disaster management. The research highlights the role of social media in shaping public discourse during crises. Its novelty lies in combining LDA-based topic modeling with sentiment analysis specifically for flash flood-related discussions on Twitter in West Sumatra. This approach provides insights into how communities communicate and perceive natural disasters through digital platforms, offering potential applications for crisis communication strategies and policy improvements in disaster response. The findings demonstrate the predominance of constructive dialogue during environmental emergencies on social media.