Acquisition of a knowledge dictionary for a text mining system using an inductive learning method

A text mining system using domain-dependent dictionaries efficiently analyzes text data. The dictionaries store not only important words for the domains, but also rules composed of some important words. This paper proposes a method that automatically acquires the rules from the text data and their classes by using an inductive learning method. The paper shows that a fuzzy inductive learning algorithm is more appropriate for the acquisition of the rules than a crisp one. Also, it shows that the method acquires rules with high accuracy through numerical experiments based on 10-fold cross validation and using daily business reports in retailing.