Arabic Text Classification: A Literature Review

Multi-label text classification is a natural extension of text classification in which each document can be assigned with a possible widespread set of labels. Natural Language Processing (NLP) helps to understand and manipulate text in natural language by using the computer. Arabic Text Classification is challenging recently because the Arabic language is under-resourced although it has many users. The aim of this paper is to build a model to classify Arabic news and help users get and display the most relevant news to their interests. In this paper, we demonstrate the efficiency of using deep learning models in solving Arabic multi-label text classification problem. Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) have been used; we build two models via python. All data has been cleaned to improve the quality of experimental data. The result of test data in LSTM was 82.03 whereas in the MLP model was 80.37, and both models were evaluated using F1 score.

Arabic text classification using deep learning models

Categorisation of text is significant trend that ultimately appears owing to the internet revolution nowadays resulting in enormous amounts data that depend on various languages. The Arabic language is one of the most commonly used languages all over the world; it is considered the fifth most spoken one. Various challenges occur through processing and classifying of Arabic text since it has more sophisticated techniques than the English language. These challenges are clear owing to the Arabic language's variation in shape, structure and component; besides, there is a lack of adequate studies discussing Arabic text classification. This research seeks to form a general point of view by categorising different techniques of Arabic text classification for helping new researches concerning this domain. Also, it shows some of prior information and innovative designs about Arabic text classification. Besides, it mentions various works that have discussed classifying Arabic text, with regard to data sets, categories and pre-processes steps, classification mechanism and assessment procedure for those techniques. These discussions aim to conclude a comprehensive overview through forming a general framework for all researchers about this domain via examining the defects of the prior studies, and then the possibility of presenting more advanced directions.

The advancement in the field of computer science, especially in machine learning (ML), represents a flourishing innovation that carries great importance in the domain of education. The beneficial impact of ML can also be observed in the realm of Qur’anic studies, particularly in Arabic text recognition and recitation analysis. This paper presents a comprehensive analysis of 34+ published scholarly articles devoted to Qur’anic studies. This work explores the convergence of machine learning methodologies and Qur’anic studies, examining the innovative applications and methodologies for Arabic text and voice classification. The fusion of ML algorithms makes the work easy and accurate to analyze, interpret, and extract valuable insights from the sacred text. Subsequently, we delve deeper into the emergent field of ML algorithms like k-NN, ANN, BLSTM, MFCC, SVM, NB and DL approaches have been adapted for Qur’anic texts classification, recitation and recitation analysis on accuracy, speed, class recognition, response rate and biasness benchmark. This work covers a diverse range of applications, including automated Qur’anic exegesis and analysis of usage of Ahkam Al-Tajweed. The main contribution of the work is to provide insight into how ML facilitates in Arabic and Kufic textual analysis, linguistic subtleties, and thematic structures of the Qur’anic text. Using the deep learning approaches, the reciters, recitation style and of the Quranic text has also explained in the work.

SVOIS: Support Vector Oriented Instance Selection for text classification

A study of the performance of embedding methods for Arabic short-text sentiment analysis using deep learning approaches

An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms

Semantic text classification: A survey of past and recent advances

This paper explicates a systematic approach of implementing text format categorization. It also emphasizes defined corpus linguistics and accordingly demonstrates how various Text files Html, Pdf, Doc and Txt format respectively could be analyzed. This work concentrates on comparing Arabic text format with English text format, for which various text formats have been considered. Hence the idea is implemented by calculating a distributed factor for the keywords distribution with respect to Arabic and English text documentation. All the text selected is from the Computer Technology domain. The text categorization process is implemented on the text collection and consists of two main corpus namely, Arabic and English text respectively. The obtained results show that the Arabic text format document is well distributed in Doc files compared to the English text document which is well distributed in Xml files. These results shall contribute in handling and building an effective Electronic Learning System for Arabic and English Texts. The results and conclusions are presented here with various graphical outputs for better understanding.

Over the last years, Natural Language Processing (NLP) for Arabic language has obtained increasing importance due to the massive textual information available online in an unstructured text format, and its capability in facilitating and making information retrieval easier. One of the widely used NLP task is “Text Classification”. Its goal is to employ machine learning technics to automatically classify the text documents into one or more predefined categories. An important step in machine learning is to find suitable and large data for training and testing an algorithm. Moreover, Deep Learning (DL), the trending machine learning research, requires a lot of data and needs to be trained with several different and challenging datasets to perform to its best. Currently, there are few available corpora used in Arabic text categorization research. These corpora are small and some of them are unbalanced or contains redundant data. In this paper, a new voluminous Arabic corpus is proposed. This corpus is collected from 16 Arabic online news portals using an automated web crawling process. Two versions are available: the first is imbalanced and contains 3252934 articles distributed into 8 predefined categories. This version can be used to generate Arabic word embedding; the second is balanced and contains 720000 articles also distributed into 8 predefined categories with 90000 each. It can be used in Arabic text classification research. The corpus can be made available for research purpose upon request. Two experiments were conducted to show the impact of dataset size and the use of word2vec pre-trained word embedding on the performance of Arabic text classification using deep learning model.

A vast amount of valuable human knowledge is recorded in documents. The rapid growth in the number of machine-readable documents for public or private access necessitates the use of automatic text classification. While a lot of effort has been put into Western languages—mostly English—minimal experimentation has been done with Arabic. This paper presents, first, an up-to-date review of the work done in the field of Arabic text classification and, second, a large and diverse dataset that can be used for benchmarking Arabic text classification algorithms. The different techniques derived from the literature review are illustrated by their application to the proposed dataset. The results of various feature selections, weighting methods, and classification algorithms show, on average, the superiority of support vector machine, followed by the decision tree algorithm (C4.5) and Naïve Bayes. The best classification accuracy was 97 % for the Islamic Topics dataset, and the least accurate was 61 % for the Arabic Poems dataset.

In this paper we present a novel method for the automatic classification of multi-label text documents. In principle, automatic classification of text is usually tackled by supervised Machine Learning techniques like Support Vector Machines (SVM), that typically achieve state-of-the-art accuracy in several domains. Nevertheless, SVM can not handle multi-labeled documents, thus a specific preprocessing of the data is needed. In this paper we present a novel technique for the transformation of multi-label data into mono-label that is able to maintain all the information, allowing the use of standard approaches like SVM. We then evaluate our system using JRC-Acquis-it, a large dataset of italian legislation that has been manually annotated according to EuroVoc, demonstrating the potential of our approach compared to the current state of the art.

Since electronic files were first introduced, automated text categorization has been a significant application and research area. Documents provide a wealth of important human information. Automated text categorization has become necessary due to the exponential increase in the number of documents that are readable by machines and available for either public or confidential access. The sheer volume of written material we handle daily makes text categorization necessary in today's world. Text categorization generally comprises two types of classification: genre-based and topic-based. Topic-based text categorization organizes texts based on their subjects, and the majority of information for genre classification comes from the internet, discussion boards, forums, and televised or print media. This document presents a comprehensive summary of the outcomes of several feature choices, classification techniques, and evaluation techniques. It offers a comprehensive overview and analysis of the research conducted on text categorization using several text classification methods.

The present study aimed to describe and evaluate the current assessment practices prevalent in the different translation courses offered at the College of Languages and Translation (COLT). A sample of specialized translation final exams in 18 translation subject areas was collected. Each final exam was analyzed in terms of the following: (1) # of English and Arabic source texts included on each final exam for each course (2) readability and difficulty level of texts included in the translation exams, (3) # of exams with a terminology subtest, (4) English and Arabic text length in words, (5) reliability, validity and discriminating power of final exams. Data analysis showed that 50% of the exams included one English text, 32% included 2 texts and 18% included 3 texts. Arabic texts were included in 73% of the exams. However, 59% of the exams included one Arabic text, 9% included two texts and 5% included 3 texts. In Addition to English and Arabic texts, 56% of the exams included a vocabulary subtest. 41% do not have any Arabic texts. The English text length ranged between 66-430 words with a median length of 181 words. The Arabic text length ranged between 26-180 with a median length of 97. The typical Flesch Reading Ease of English texts was 40 and the typical Flesch-Kincaid Grade level score was 11. There were no significant differences among the different college levels nor different subject areas in text length or text difficulty level. Translation exams currently used at COLT lack validity, reliability, and discriminating power. Some reasons for lack of reliability and validity are given. Students’ views on translation exams are also reported. A model for more valid, reliable, and discriminating translation exams is given with students views of it as well.

Due to the era of Big Data and the rapid growth in textual data, text classification becomes one of the key techniques for handling and organizing the text data. Feature selection is the most important step in automatic text categorization. In order to choose a subset of available features by eliminating unnecessary features to the classification task, a novel text categorization algorithm called chaos genetic feature selection optimization is proposed. The proposed algorithm selects the optimal subsets in both empirical and theoretical work in machine learning and presents a general framework for text categorization. Experimental results show that the proposed algorithm simplifies the feature selection process effectively and can obtain higher classification accuracy with a smaller feature set.