Automatic Arabic text categorization: A comprehensive comparative study

Abstract

Text categorization or classification (TC) is concerned with placing text documents in their proper category according to their contents. Owing to the various applications of TC and the large volume of text documents uploaded on the Internet daily, the need for such an automated method stems from the difficulty and tedium of performing such a process manually. The usefulness of TC is manifested in different fields and needs. For instance, the ability to automatically classify an article or an email into its right class (Arts, Economics, Politics, Sports, etc.) would be appreciated by individual users as well as companies. This paper is concerned with TC of Arabic articles. It contains a comparison of the five best known algorithms for TC. It also studies the effects of utilizing different Arabic stemmers (light and root-based stemmers) on the effectiveness of these classifiers. Furthermore, a comparison between different data mining software tools (Weka and RapidMiner) is presented. The results illustrate the good accuracy provided by the SVM classifier, especially when used with the light10 stemmer. This outcome can be used in future as a baseline to compare with other unexplored classifiers and Arabic stemmers.

A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

“…The results indicate that SVM achieved the best accuracy at 72%. Another comparative study of Arabic text classifications algorithms using various Arabic stemmers is presented in Hmeidi et al [Hmeidi, Al-Ayyoub, Abdulla et al (2015)]. The authors compare four supervised classification algorithms: SVM, NB, KNN, Decision Tress, and Decision Tables.…”

Developing the Digital Artifact for the Sustainability of Marketing Research Results

“…It concentrates on the discussion of topic detection and classification from the perspective of text mining analysis. Referring to Ismail Hmeidi et al, text classification, usually referring to text categorization, is defined as a process of "classifying an unstructured text document in its desired category(s) depending on its contents" [64]. Among methods of text classification, automatic keyword extraction is an important research direction in text mining and natural language processing because it enables us to summarize the entire document [65,66].…”

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“…Moreover, Hmeidi et al [12] studied the influence of raw text, khoja root-based stemmer and light stemming of Arabic text documents based on standard classifiers, such as NB, SVM, KNN, J48 and Decision Table classifiers. The results exhibited that the SVM and NB classifiers with light stemming provides better classification accuracy than other classifiers.The same conclusion was drawn up by Al-Badarneh [13] and Ayedh et al [14] by using various pre-processing methods.…”