ABSTRACT
This paper presents an analysis of _Affect, _!lehavior and .Gognition (ABC) of people who have experienced disasters, namely, natural, human induced, and pandemic. The goal is to understand their attitudes in disaster risk situations. We used secondary data from personal stories, research and media articles as corpus, sourced from digital libraries and the Web. The textual data was analyzed using Latent Semantic Analysis and Leximancer, to explore their capability in ABC mining. The classification of ABC was performed manually by two classifiers; there was substantial agreement between them. Univariate Analysis of Variance (ANOVA) was performed on the ABC data. The results showed that both text mining tools were similar in identifYing ABC words. However, there were highly significant effects of disaster and corpus types on the frequency of ABCs. The findings were used to refine the citarasa model.
