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Efficient English text classification using selected Machine Learning Techniques
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DOI：10.1016/j.aej.2021.02.009.png)
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摘要
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Text classification (TC) is an approach used for the classification of any kind of documents for the target category or out. In this paper, we implemented the Support Vector Machines (SVM) model in classifying English text and documents. Here we did two analytical experiments to check the selected classifiers using English documents. Experimental results performed on a set of 1033 text document present that the Rocchio classifier provides the best performance results when the size of the feature set is small while SVM outperforms the other classifiers. From the experimental analysis, we observed that the classification rate exceeds 90% when using more than 4000 features. (C) 2021 THE AUTHOR. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword:
Text classification
English language
Machine Learning
Text mining
Support Vector Machines
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