Surveys are an important part of marketing and customer relationship management, and open answers (i.e., answers to open questions) in particular may contain valuable information and provide an important basis for making business decisions. We have developed a text mining system that provides a new way for analyzing open answers in questionnaire data. The product is able to perform the following two functions: (A) accurate extraction of characteristics for individual analysis targets, (B) accurate extraction of the relationships among characteristics of analysis targets. In this paper, we describe the working of our text mining system. It employs two statistical learning techniques: rule analysis and Correspondence Analysis for performing the two functions. Our text mining system has already been put into use by a number of large corporations in Japan in the performance of text mining on various types of survey data, including open answers about brand images, open answers about company images, complaints about products, comments written on home pages, business reports, and help desk records. In this it has been found to be useful in forming a basis for effective business decisions.

We address the issue of 'topic analysis,' by which is determined a text's topic structure, which indicates what topics are included in a text, and how topics change within the text.We propose a novel approach to this issue, one based on statistical modeling and learning.We represent topics by means of word clusters, and employ a finite mixture model to represent a word distribution within a text.Our experimental results indicate that our method significantly outperforms a method that combines existing techniques.

We propose a new method of text classification using stochastic decision lists. A stochastic decision list is an ordered sequence of IF-THEN rules, and our method can be viewed as a rule-based method for text classification having advantages of readability and refinability of acquired knowledge. Our method is unique in that decision lists are automatically constructed on the basis of the principle of minimizing Extended Stochastic Complexity (ESC), and with it we are able to construct decision lists that have fewer errors in classification. The accuracy of classification achieved with our method appears better than or comparable to those of existing rule-based methods.

We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model. Experimental results indicate that our method outperforms existing methods.