Abstract
While the open-ended questionnaire method is a good means to collect free expressions of opinion, the analysis of collected questionnaires is usually done manually, and thus is costly. Furthermore, the results derived from such humans' judgments tend to lack objectivity. Given this background, we are exploring computational approaches to the automatic classification of collected open-ended questionnaires. This paper reports the results of our preliminary experiments, where we used the maximum-entropy model for questionnaire classification. The results show that our method works well for extracting discriminative linguistic expressions for each response type such as proposal, demand, approval, opposition, etc., and can produce questionnaire clusters analogous to those produced by humans.
