C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Textual data mining of service center call records
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Acquisition of a Knowledge Dictionary from Training Examples Including Multiple Values
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Combining clustering and co-training to enhance text classification using unlabelled data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Expert Systems with Applications: An International Journal
How to design and utilize online customer center to support new product concept generation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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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.