Visualization and analytical support of questionnaire free-texts data based on HK Graph with concepts of words

A lot of companies carry out questionnaires. These questionnaires often have questions which need respondents to answer by free description. It is, however, inefficient for an analyzer to read whole texts to get outlines or classify them, or it is difficult to correctly analyze them without subjective biases. The authors have proposed "HK Graph" (Hierarchical Keyword Graph) which is a support tool for text mining. HK Graph can visualize the relationships among attributes and words with hierarchical graph structure based on frequency of co-occurrence. However, the result of HK Graph is not enough helpful for the analyzer to grasp the outlines of the texts and extract opinions from them, because it regards divided words as different ones unless they perfectly match and that makes the visualized result complicated. This paper presents a new visualization method for the HK Graph incorporating an aggregating words method based on concepts of words. An experiment is carried out by applying the proposed method to actual questionnaire data on disasters and studies the effectiveness of the proposed method.