A Classifier Module for Analyzing Community Responses on Disaster Preparedness

This paper was presented at the Proceedings of the 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management

The International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) is one of the premier international technical conferences of the Asia Pacific Region. HNICEM has been held since 2003 and provides an important forum for researchers and engineers from industry, and professors and graduate students from the academia to network and to discuss new ideas and development in emerging areas of electrical and electronic engineering, computers science and related topics. The conference features plenary and invited talks by eminent scientists and engineers, tutorials, paper presentations and poster sessions.

Authors:

  • Angelica Dela Cruz
  • Nathaniel Oco
  • Rachel Edita Roxas

Abstract:

In this paper, we implemented, trained, and evaluated a module to automatically classify community responses of respondents of an e-participation toolkit on disaster preparedness. Responses were automatically classified into 10 different categories, which were derived from a codebook created for the content analysis of the responses. Results show that most responses were classified in the category Local Government Unit (LGU) accountability. The classifier achieved a 93.3% mean accuracy with ten-fold cross validation. This analysis shows that the created classifier module can successfully decode responses and can be further improved by applying multi-label classification. However, the classifier achieved 63.8% agreement with the manual coding, and a revision of the code book may be deemed necessary as a future work. The application of pre-processors such as spelling checkers can also be considered.

Click here for the link to the paper.

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