Akihiro Suyama

An e-mail analysis method based on text mining techniques

This paper proposes a method employing text mining techniques to analyze e-mails collected at a customer center. The method uses two kinds of domain-dependent knowledge. One is a key concept dictionary manually provided by human experts. The other is a concept relation dictionary automatically acquired by a fuzzy inductive learning algorithm. The method inputs the subject and the body of an e-mail and decides a text class for the e-mail. Also, the method extracts key concepts from e-mails and presents their statistical information. This paper applies the method to three kinds of analysis tasks: a product analysis task, a contents analysis task, and an address analysis task. The results of numerical experiments indicate that acquired concept relation dictionaries correspond to the intuition of operators in the customer center and give highly precise ratios in the classification.

Rule discovery from textual data based on key phrase patterns

This paper proposes a new method for discovering rules from textual data. The method decomposes textual data into word sets by using lexical analysis, generates training examples from both key phrase relations extracted from the word sets by using key phrase patterns and text classes given by the user, and acquires key phrase relation rules from the examples by using a fuzzy inductive learning algorithm. The method is also able to deal with textual data that requires word segmentation, such as Japanese text. This paper reports on the application of the method to e-mail analysis tasks for a customer center. The e-mails are written in Japanese and have two analytical criteria: a product criterion and a contents criterion. We evaluate the acquired rules in each criterion.

Inductive Learning of a Knowledge Dictionary for a Text Mining System

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. The paper proposes a method that automatically acquires the rules from the text data and their classes by using a fuzzy inductive learning method. Also, in order to infer a class corresponding to new text data, the paper proposes an inference method based on the acquired fuzzy decision tree. Moreover, the efficiency of the methods is verified through numerical experiments using more than 1,000 daily business reports concerning retailing.

Acquisition of a Knowledge Dictionary from Training Examples Including Multiple Values

A text mining system uses two kinds of background knowledge: a concept relation dictionary and a key concept dictionary. The concept relation dictionary consists of a set of rules. We can automatically acquire it by using an inductive learning algorithm. The algorithm uses training examples including concepts that are generated by using both lexical analysis and the key concept dictionary. The algorithm cannot deal with a training example with more than one concept in the same attribute. Such a training example is apt to generate from a report, when the concept dictionary is not well defined. It is necessary to extend an inductive learning algorithm, because the dictionary is usually not completed. This paper proposes an inductive learning method that deals with the report. Also, the paper shows the efficiency of the method through some numerical experiments using business reports about retailing.