Arabic Text Classification Based on Features Reduction Using Artificial Neural Networks

Arabic text classification methods: Systematic literature review of primary studies

“…TC algorithms require that text features are formatted before they can be interpreted by the specified classifier, this process is also referred to as term weighting because each term is entered together with a weight value. Included papers show the most used technique is the Term Frequency-Inverse Document Frequency (TF-IDF) as in [27,32,37,40,43,45,48,51,53,55,57,58,[60][61][62]67]. It is a statistical method to indicate the significance of a word within a given corpus.…”

Text Classification Based on Weighted Extreme Learning Machine

“…-identifying the term significance is very useful in TC system, that can be done by weighting term which can be calculated by multiplying TF by the IDF [2] Which can be calculated as in Equation 1.…”

“…It has been applied effectively on various occasions and is incorporated in our regular day to day existences. For example, daily paper articles and scholastic papers are frequently composed by subject or field [2]. TC gives a solution in consequently arranging innumerable articles and papers.…”

Arabic Text Categorization Using Support vector machine, Naïve Bayes and Neural Network

“…Third, dimension reduction, in this step, the most useful and valuable features for classification are selected. Most commonly selection methods are Chi square and document frequency, information gain and mutual information [14,15,16,17] Usually text documents are represented as a vector of term weights (word features). One major problem in classification for Arabic and English language is the high dimensionality of text documents.…”