부산대학교 도서관

In this paper, we explore the task of using topic modeling as an automated approach to analyze opinions, ideas, and other inputs on disaster risk reduction (DRR) gathered from local communities that have been victims of Philippine disasters. In particular, we used Latent Dirichlet Allocation (LDA) algorithm and k-means clustering with TF-IDF, and examined topics surfacing from such outputs. Our topics show that respondents from these disaster-stricken communities focus their concerns more on improving their barangay's disaster response and preparedness. The results of both LDA and k-means clustering with TF-IDF showed similarity up to a certain degree. The resulting analyses can be presented to stakeholders towards developing policies and procedures to mitigate the effects of disasters. Future works include increasing the data size and automating topic labeling.