Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Textual data mining of service center call records
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
One-class svms for document classification
The Journal of Machine Learning Research
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
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This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item includes various viewpoints and each viewpoint is regarded as a class. The method inductively acquires classification models for 2-class classification tasks from items labeled by multiple classes. The method infers classes of new items by using these models. Lastly, the method extracts important expressions from new items in each class and extracts characteristic expressions by comparing the frequency of expressions. This paper applies the method to questionnaire data described by guests at a hotel and verifies its effect through numerical experiments.