A survey of Arabic text classification approaches

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.

Automatic text classification or categorization consists to assign predefined classes or categories to a given set of text documents aiming to organize the document collection based on conceptual views. Although there are many text classifiers in the literature, most of them are assessed using English or other non-Arabic languages text collections. The lack of availability of a large collection in the Arabic language is one of the most important challenges facing the few numbers of existing Arabic text classifiers (ATC). We present in this paper a literature review in the domain of Arabic text classification. We firstly overview the ATC based on machine learning algorithms. Then, we investigate ATC based on deep learning techniques as well as a set of other classifiers based on non-ML algorithms. The assessment of these ATC is also discussed. Finally, we focus on some open problems and we suggest some future directions.

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

Natural language processing (NLP) techniques have evolved substantially in recent years, with transformer-based architectures emerging as powerful tools for textual analysis across various fields. This paper investigates the underexplored area of Arabic legal text classification through the implementation of transformer-based language models integrated with deep learning (DL) architectures. Even though transformer models are widely used in legal NLP for high-resource languages, their application to Arabic legal texts remains limited due to linguistic complexities and dataset scarcity. We introduce a classification model for Arabic legal text and present a new dataset of over 27,000 Moroccan legal cases with seven categories. Our methodology implements a segment-based approach to overcome the token limitations of transformer models, processing document segments individually before aggregating embeddings to create complete document representations. Four deep learning architectures are then used to classify those documents. Experimental evaluations demonstrate that MARBERTv2 combined with Gated Recurrent Unit (GRU) architecture achieves superior performance with 95.01% accuracy and Matthews Correlation Coefficient (MCC) of 0.9090.

Arabic text classification using deep learning models

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.

This paper presents our system developed for the AbjadNLP Shared Task 4 on Medical Text Classification in Arabic, which aims to assign Arabic medical question-answer pairs to a predefined set of medical categories.The task poses significant challenges due to severe class imbalance across 82 categories and the linguistic complexity of domain-specific Arabic medical text.To address these challenges, we propose an imbalance-aware training framework that combines targeted data augmentation for minority classes with class-weighted focal loss during fine-tuning.We evaluate multiple Arabic pretrained transformer models under a unified training configuration and further improve robustness through a majorityvoting ensemble of the best-performing models.Our approach achieves competitive performance, ranking 15th on the private leaderboard with a macro F1 score of 0.4052, demonstrating the effectiveness of combining different data augmentation techniques, imbalanceaware training objectives, and ensemble learning for large-scale, highly imbalanced Arabic medical text classification.

Information extraction from Arabic tweets has attracted the attention of researchers due to the huge data accessibility for the swift expansion of social media platforms. With the increasing use of social web applications, information extraction from the various platforms has gained importance for understanding the trending post and events predictions based on those sentiments written by the users on certain news feeds. The Arabic Language is mostly used in Middle Eastern and African countries and most users tweet on social media using the Arabic language, therefore Arabic text classification and sentiment analysis aimed to predict information extraction from social media platforms. This research provides a more detailed critical review of the information extraction presented in the literature focused on using different tools, methods, and techniques like k-NN, support vector machines, Naïve Bayes, and other machine learning tools for the data extraction and processing.

Artificial Intelligence (AI) has been used widely to extract people's opinions from social media websites. However, most of the existing works focus on eliciting the features from English text. In this paper, we describe an Arabic text sentiment analysis approach using a Deep Neural network, namely Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). In this research, we investigate how the different pre-trained Word Embedding (WE) models affect our model's accuracy. The dataset includes Arabic corpus collected from Twitter. The results show significant improvement in Arabic text classification.

The list of features and text representation model that is utilized are critical components that determine the performance and accuracy of text categorization. Feature selection is a technique for improving classification accuracy and processing efficiency by removing redundant or superfluous words (features) and keeping only those that give vital information that assists in categorization. This paper presents the recent researches that studied the impact of feature selection methods on Arabic text classification.

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

Deep learning has become one of the crucial trends in the modern era due to the huge amount of data that has become available. This paper aims to investigate and improve a generic framework for Arabic Text Classification (ATC) with different deep learning techniques. Besides, it deals directly with a word in its original style as a basic unit of modern Arabic sentence and on a different level of N-grams versus a combination of Intersected Consecutive Word proposed method (ICW). However, it aimed to discuss the results of the different experiments for the enhancements of the proposed method on different deep learning algorithms such as Scaled Conjugate Gradient (SCG) and Gradient descent with momentum and adaptive learning rate backpropagation (GDX) on ATC. The results showed that the proposed framework applied with the SCG algorithm and TF-IDF outperforms the GDX algorithm with an accuracy ratio of 90.65%.

<p>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</p>

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.

Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.