Discovering Topics from Qualitative Responses of a Disaster Preparedness e-Participation System


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The paper was presented at the 2017 IEEE Region 10 Conference (TENCON 2017)

TENCON 2017 is expected to bring together researchers, educators, students, practitioners, technocrats and policymakers from across academia, government, industry and non-governmental organizations to discuss, share and promote current works and recent accomplishments across all aspects of electrical, electronic and computer engineering, as well as information technology. Distinguished people will be invited to deliver keynote speeches and invited talks on trends and significant advances in the emerging technologies.


  • Joyce Emlyn Guiao
  • Jennifer Carreon
  • Alvin Malicdem
  • Nathaniel Oco
  • Rachel Edita Roxas
  • Brandie M. Nonnecke
  • Shrestha Mohanty
  • Andrew Lee
  • Jonathan Lee
  • Justin Mi
  • Sequioa Beckman
  • Cammile Crittenden
  • Ken Goldberg


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.

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