Cross Tabulation Analysis of Resident Questionnaire Data based on Tensor Decomposition

Abstract

In recent years, the issues faced by local communities have become increasingly complex, and the importance of accurately understanding residents' opinions and needs has been growing. In the analysis of resident questionnaires aimed at identifying the issues that residents consider important in their communities, cross tabulation, which aggregates combinations of two questions, is commonly used as a method to clarify inter-question relationships. However, cross tabulation has the limitation that it becomes difficult to interpret the results and discover important relationships as the number of question combinations increases. This study proposes a method that treats multiple cross tabulation results as tensor data, extracts major response patterns based on nonnegative CP decomposition, and clusters these patterns. We also report the analysis results obtained by applying the proposed method to actual resident questionnaire data.