Arabic Text Mining

The rapid growth of the internet has increased the number of online texts. This led to the rapid growth of the number of online texts in the Arabic language. The enormous amount of text must be organized into classes to make the analysis process and text retrieval easier. Text classification is, therefore, a key component of text mining. There are numerous systems and approaches for categorizing literature in English, European (French, German, Spanish), and Asian (Chinese, Japanese). In contrast, there are relatively few studies on categorizing Arabic literature due to the difficulty of the Arabic language. In this work, a brief explanation of key ideas relevant to Arabic text mining are introduced then a new classification system for the Arabic language is presented using light stemming and Classifier Na\"ive Bayesian (CNB). Texts from two classes: politics and sports, are included in our corpus. Some texts are added to the system, and the system correctly classified them, demonstrating the effectiveness of the system

Identification of Critical Parameters Affecting an E-Learning Recommendation Model Using Delphi Method Based on Expert Validation

E-learning is an innovative strategy for enhancing teaching and learning in digital environments with the goal of enhancing education. In the same context, recommendation models have been developed for predicting the user’s learning preferences. A task that has become urgently necessary is enhancing the learning process by designing recommendation models for e-learning software that then helps users choose the most pertinent learning materials (contents) from a wide number of sources. The general consensus is that designing a recommendation model for e-learning is influenced by parameters that are related to e-learning, and much effort has been exerted to determine those parameters. However, no agreement has been reached as to what constitutes such parameters. Keeping this issue in mind, this study aims to identify the parameters that should be considered when generating e-learning recommendations in developing countries. On the basis of the relevant literature, with the use of the Delphi method and with aid from e-learning experts, this paper identifies ten critical parameters related to e-learning. The results show that perceived ease of use is the most critical parameter out of the ten e-learning-related parameters, while user preference is the parameter that contributes least to e-learning