Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Sakurai, S., Goh, C., Orihara, R. (2005). Analysis of Textual Data with Multiple Classes. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_12
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DOI: https://doi.org/10.1007/11425274_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
Online ISBN: 978-3-540-31949-8
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