A Closer Look at Arabic Text Classification

Abstract

The world has witnessed an information explosion in the past two decades. Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices. This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level. Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information. Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature. Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used. Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others. Arabic is the fifth most spoken language in the world. There has been many works in the literature for Arabic text classification. However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades. The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification. We have covered over 50 research papers covering the past two decades (2000 -2019). The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including. 2) New Techniques. 3) Most claimed efficient technique. 4) Datasets used and which ones are most popular. 5) Which feature selection techniques are used? 6) Popular classes/categories used. 7) Effect of stemming techniques on classification results.

A systematic review of text classification research based on deep learning models in Arabic language

“…The Arabic language consists of 28 letters, including hamzas and elmodod. Each letter has certain structures that depend on the position of the letter in the whole, that is, whether it is at the beginning of the word, in the middle of the word, or at the end of the word [34]. There are no upper-and lower-case letters in Arabic unlike English or other languages [31].…”

“…But when it comes to the Arabic language, the application of these algorithms does not give similar accuracy and validity in classification. Therefore, appropriate algorithms have been created to synthesize words in the Arabic language, but they have not yet demonstrated fully reliable merit for the classification process [34].…”

“…Techniques which rely on mathematical rules and principles are also called statistical text classification. These techniques are suitable for small data, such as "Frequentist procedures, Bayesian procedures, and the Binary and multiclass procedures" [34].…”

“…Therefore, machine learning classification techniques appeared which aim to classify unstructured texts and documents based on certain algorithms designed for this purpose. Machine learning techniques can be divided into groups as shown in Figure 2 [34]. In the Supervised Learning technique, different data are recognized, whether visual, audio, or text.…”

“…Prefixes and suffixes are also removed, and all words of the same root are grouped. This stage aims to give precision to the classification process and save time [34]. After removing all of the appendages, the word is returned to the root by three methods: "the root-based stemmer, the light stemmer, and the statistical stemmer."…”