Arabic Text Mining Using an Associative Approach Computer Science Essay
Abstract. Text classification is the most important research question in the field of data mining. The main idea of using the stem technique is to reduce the number of functions that are possible. Sentiment analysis and web text mining techniques. In this. research, a large dataset corpus has been collected to identify. and classify anti-Islamic online content. Our goal is to. In 2014, Abbas and Al-Qaza presented AAES for "automated Arabic essay scoring", a web-based system based on Vector Space Model VSM. The system is intended to score short essay-like answers to questions. The two-phase system initially applies IR to extract important information from the essays. Authors Odeh and Al-Najdawi 37 proposed a new associative classification algorithm based on the Na ve Bayes model, called ACNB. The ACNB algorithm works through three main steps. We will examine and evaluate some commonly used methods, using web mining systems that deal with problems in language-specific text processing. Arabic language independent algorithm will be. Text classification is one of the methods used to manage, organize and retrieve the necessary data from the vast amount of text available. Several methods have been proposed to manipulate the text classification problem. In recent years, some studies have proposed the use of the AC approach to associative classification. This article examines sentiment analysis, also known as opinion mining, the process of determining the attitudes of textual opinions, whether positive, negative, or neutral. Data sparsity represents a Shehab et al. 18 applied four text similarity measures, two for string-based algorithms and two for corpus-based algorithms. Arab students answer from an internal data set for scoring.