An Automated Arabic Text Categorization Based on the Frequency Ratio Accumulation

Compared to other languages, there is still a limited body of research which has been conducted for the automated Arabic Text Categorization (TC) due to the complex and rich nature of the Arabic language. Most of such research includes supervised Machine Learning (ML) approaches such as Naïve Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine and Decision Tree. Most of these techniques have complex mathematical models and do not usually lead to accurate results for Arabic TC. Moreover, all the previous research tended to deal with the Feature Selection (FS) and the classification respectively as independent problems in automatic TC, which led to the cost and complex computational issues. Based on this, the need to apply new techniques suitable for Arabic language and its complex morphology arises. A new approach in the Arabic TC term called the Frequency Ratio Accumulation Method (FRAM), which has a simple mathematical model is applied in this study. The categorization task is combined with a feature processing task. The current research mainly aims at solving the problem of automatic Arabic TC by investigating the FRAM in order to enhance the performance of Arabic TC model. The performance of FRAM classifier is compared with three classifiers based on Bayesian theorem which are called Simple NB, Multi-variant Bernoulli Naïve Bayes (MNB) and Multinomial Naïve Bayes models (MBNB). Based on the findings of the study, the FRAM has outperformed the state of the arts. It's achieved 95.1% macro-F1 value by using unigram word-level representation method