Abstract
This study aims to put forward a comprehensive and detailed classification system to categorize different Arabic-speaking website pages with unscrupulous intentions and questionable language. The methodology of this is based on a quantitative approach by using different algorithms (supervised) to build a model for data classification by using manually categorized information. The classification algorithm used to construct the model uses quantitative information extracted by Posit or SAFAR textual analysis framework. This model functions with (58) features combined from Posit - n-grams and morphological SAFAR V2 POS tools. This model achieved more than (94 %) success in the level of precision. The results of this study revealed that the best results reaching 94% precision have been achieved by combining Posit + SAFAR + (18 attributes Posit+ SAFAR N-Gram). Moreover, the most reliable results have been achieved by applying a Random Forest classification algorithm using regression. The research recommends working more on this topic and using new algorithms and techniques.