Text Categorization is a technique for assigning documents based on their contents to one or more pre-defined categories. Achieving highest categorization accuracy remains one of the major challenges and it is also time consuming. We proposed approach to tackle these challenges. The proposed approach uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Its features are represented using bag of word technique and an improved Term Frequency (TF) technique is used in features selection. The proposed approach is tested with known datasets. The experiments are done without both of normalization and stemming, with one of them, and with both of them. The obtained results of proposed approach are generally improved compared to existing techniques.The performance attributes of proposed Arabic Text Categorization approach were considered: Accuracy, Recall, Precision and F-measure (F1). The averages of the obtained results are 97.50%, 97.50%, 97.51%, and 97.49% respectively using normalization.

Abstract-There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.

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