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.

A Comprehensive Overview of Recommender System and Sentiment Analysis

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.