Arabic Text Mining

TL;DR

This paper introduces a new Arabic text classification system using light stemming and Naive Bayesian classifier, demonstrating effective categorization of politics and sports texts despite language complexity.

Contribution

It presents a novel Arabic text classification approach combining light stemming with Naive Bayesian classifier, addressing the scarcity of Arabic-specific text mining methods.

Findings

System correctly classified new texts from politics and sports.

Effective classification achieved with the proposed approach.

Addresses challenges of Arabic language in text mining.

Abstract

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.