Support Vector Machine versus k-Nearest Neighbor for Arabic Text Classification

Author(s): Eman Al-Thwaib, Waseem Al-Romimah

Text Classification (TC) or text categorization can be described as the act of assigning text documents to predefined classes or categories. The need for automatic text classification came from the large amount of electronic documents on the web. The classification accuracy is affected by the documents content and the classification technique being used. In this research, an automatic Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifiers will be developed and compared in classifying 800 Arabic documents into four categories (sport, politics, religion, and economy). The experimental results are presented in terms of F1-measure, precision, and recall.

Text Classification, Machine Learning, Support Vector Machine, k-Nearest Neighbor

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