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2015 | OriginalPaper | Chapter

The Effect of Stemming and Stop-Word-Removal on Automatic Text Classification in Turkish Language

Authors : Mustafa Çağataylı, Erbuğ Çelebi

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Text classification is defined simply as the labeling of natural and unstructured language text documents using predefined categories or classes. This classification not only help organizations in improving their business communication skills and their customer satisfaction levels, but also improves the usage of unstructured data in academic and non-academic world. The aim of this study is to analyze the effect of stemming, over-sampling, and stopword-removal when doing automatic classification on Turkish content. After obtaning a Turkish Corpus, stemming, balancing, and stopword-removal is applied and the results are evaluated.

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previous chapter On Measuring the Complexity of Classification Problems
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Metadata
Title
The Effect of Stemming and Stop-Word-Removal on Automatic Text Classification in Turkish Language
Authors
Mustafa Çağataylı
Erbuğ Çelebi
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26531-5

Electronic ISBN: 978-3-319-26532-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26532-2_19

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