Open AccessArabic Text Classification: A Review
Author(s) -
Adel Hamdan Mohammad
Publication year - 2019
Publication title -
modern applied science
Language(s) - English
Resource type - Journals
eISSN - 1913-1852
pISSN - 1913-1844
DOI - 10.5539/mas.v13n5p88
Subject(s) - computer science , arabic , weighting , artificial intelligence , classifier (uml) , decision tree , natural language processing , naive bayes classifier , support vector machine , set (abstract data type) , data mining , information retrieval , machine learning , linguistics , medicine , philosophy , radiology , programming language
Text classification is an important topic. The number of electronic documents available on line is massive. Text classification aims to classify documents into a set of predefined categories. Number of researches conducted on English dataset is great in comparison with number of researches done using Arabic dataset. This research could be considered as reference for most researchers who deal with Arabic dataset. This research used the most well-known algorithms used in text classification with Arabic dataset. Besides that, dataset used in this research is large enough in comparison with most dataset for Arabic language used in other researches. In addition, this research used different selections and weighting methods for documents. I expect that all researchers who would write researches using Arabic dataset will find this work helpful. Algorithms used in this research are naïve Bayesian, support vector machines, artificial neural networks, k- nearest neighbors, C4.5 decision tree and rocchio classifier.