ANALISIS RESPON PUBLIK DAN PERMODELAN TOPIK MENGGUNAKAN LATENT DIRICHLET ALLOCATION METHOD (LDA) PADA BENCANA BANJIR BANDANG DI SUMATERA BARAT 2024 MELALUI TWITTER
DOI:
https://doi.org/10.20961/h5qqjh18Keywords:
Latent Dirichlet Allocation, LDA, topic modeling, sentimentAbstract
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.
References
Albuquerque, J. P., Herfort, B., Brenning, A., & Zipf, A. (2015). A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, 29(4), 667-689.
Amen, B., Faiz, S., & Do, T. T. (2022). Big data direct,ed acyclic graph model for 178 real- time COVID-19 twitter stream detection. Pattern Recognition, 123, 108404. https://doi.org/10.1016/j.patcog.2021.108404
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242-261
Chen, Y., He, S., & Zhou, Z. (2022). Investigation of social media representation bias in disasters: Towards a systematic framework. International Journal of Disaster Risk Reduction, 81, 103312. https://doi.org/10.1016/J.IJDRR.2022.103312
Chowdhury, S. (2013). Tweet4act: Using incident-specific profiles for classifying crisis- related messages. In ISCRAM (pp. 366-370). Baden-Baden, Germany.
Cameron, M. (2012). Emergency situation awareness from Twitter for crisis. In SWDM 2012 workshop held jointly with WWW (pp. 697-700).
A. H., Giles, L., & Jansen, B. J. (2014). Mapping moods: Geo-mapped sentiment analysis during hurricane sandy. In Proceedings of the 11th international conference on information systems for crisis response and management (ISCRAM 2014) (pp. 642-651).
DiCarlo, M. F., & Berglund, E. Z. (2021). Connected communities improve hazard response: An agent-based model of social media behaviors during hurricanes. Sustainable Cities and Society, 69(June 2020), 102836. https://doi.org/10.1016/j.scs.2021.102836
Girolami, M., & Kabán, A. (2003). On an equivalence between PLSI and LDA. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 433-434).
Hikmah, H., Asrirawan, A., Apriyanto, A., & Nilawati, N. (2023). Peramalan Data Cuaca Ekstrim Indonesia Menggunakan Model ARIMA dan Recurrent Neural Network. Jambura Journal of Mathematics, 5(1), 230– 242. https://doi.org/10.34312/jjom.v5i1.17496
Haryoko, U., & Gunawan, D. (2022). Analisis Hujan Agustus 2022. Buletin Informasi Iklim September, 9, 1–7
Imperial, J. M., Hermocilla, J. Z., Caro, J. E. C., & Aggabao, J. A. (2019). Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks. arXiv preprint arXiv:1908.01765. Retrieved from https://arxiv.org/abs/1908.01765
Kryvasheyeu, Y., Chen, H., Moro, E., Hentenryck, P. V., & Cebrian, M. (2016). Performance of social network sensors during Hurricane Sandy. PLOS ONE, 11(2), e0145123.
Kemp, S. (2020, January 30). We Are Social. Retrieved May 19, 2020, from https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social- media.
Koto, F., & Rahmaningtyas, G. Y. (2017). InSet Lexicon: Evaluation of a Word List for Indonesian Sentiment Analysis in Microblogs. In Proceedings of the 21st International Conference on Asian Language Processing (IALP), Singapore. IEEE
Li, L., Du, Y., Ma, S., Ma, X., Zheng, Y., & Han, X. (2023). Environmental disaster and public rescue: A social media perspective. Environmental Impact Assessment Review, 100, 107093. https://doi.org/10.1016/J.EIAR.2023.107093
Muralidharan, S., Rasmussen, L., Patterson, D., & Shin, J. H. (2011). Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts. Public Relations Review, 37(2), 175–177. https://doi.org/10.1016/j.pubrev.2011.01.010
Scarborough, W. J. (2018). Feminist Twitter and Gender Attitudes: Opportunities and Limitations to Using Twitter in the Study of Public Opinion. Socius, 4, 1–16. https://doi.org/10.1177/2378023118780760
Saragih, I. J. A., Sirait, M., & Sari, D. A. (2021). Deskripsi Opini Publik tentang Bencana Alam untuk Rencana Studi Mitigasi di Indonesia (Studi kasus: Bencana Hidrometeorologi). MKGI: Jurnal Meteorologi, Klimatologi Geofisika Dan Instrumentasi, 1(1), 33–39
Sakaki, T., Okazaki, M., & Matsuo, Y. (2013). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919-931.
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Vo, B. K. H., & Collier, N. (2013). Twitter emotion analysis in earthquake situations. International Journal of Computational Linguistics and Applications, 4(1), 159- 173.
Wallach, H. M., Mimno, D., & McCallum, A. (2009). Rethinking LDA: Why priors matter. In Proceedings of the 22nd International Conference on Neural Information Processing Systems.
Wahyudi, M., Rahman, A. A., & Rizal, M. (2023). Respon Nelayan terhadap Fenomena Iklim ( Perspektif Sosial Ekonomi ). Journal on Education, 05(04), 16748–16758
Vishwanath, T., Shirwaikar, R. D., Jaiswal, W. M., & Yashaswini, M. (2023). Social media data extraction for disaster management aid using deep learning techniques. Remote Sensing Applications: Society and Environment, 30, 100961. https://doi.org/10.1016/J.RSASE.2023.100961
Zhang, L., Li, H., & Chen, K. (2019). Emergency response to a flood disaster in a highly urbanized region: Lessons learned from the 2012 Beijing flood. Natural Hazards, 90(1), 441-460.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Indonesian Journal of Environment and Disaster
This work is licensed under a Creative Commons Attribution 4.0 International License.
