A Survey of Arabic Text Classification Models

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.

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

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 study investigates the role of artificial intelligence (AI) in Arabic language processing, focusing on both its developmental potential and the challenges that threaten the language’s integrity and identity. Despite progress in natural language processing (NLP), the Arabic language still faces substantial barriers due to its intricate grammatical rules and semantic depth, which make it more complex to integrate into AI systems compared to other languages. In addition to technical obstacles, the application of AI raises ethical concerns—particularly around preserving the authenticity of Arabic and protecting intellectual property. Issues such as uncredited AI-generated content and reduced human involvement in language formulation may compromise the credibility and originality of information. The study aims to offer future-oriented solutions that address these ethical challenges while promoting a balanced approach between leveraging AI technologies and safeguarding the linguistic heritage of Arabic. Adopting a qualitative, literature-based methodology with a focus on ethical, linguistic, and technological analysis, the research underscores the urgent need for ethical and regulatory frameworks to guide AI implementation in this domain. The findings highlight the importance of continuous investment in research and development to support Arabic’s sustainable integration into AI-driven platforms. As a contribution, the study provides a conceptual foundation for responsible innovation, advocating for policies that protect the identity of the Arabic language while enabling its evolution within the digital age

This article provides an insightful exploration into the burgeoning field of Artificial Intelligence (AI) in the domain of Arabic language learning. With the rapid advancements in AI technologies, there has been a notable surge in interest and research focusing on its application within language education, particularly for less commonly taught languages like Arabic. This paper synthesizes the current state of AI in Arabic language learning, discussing various AI-driven approaches, tools, and applications employed in this domain. Furthermore, it highlights the challenges and opportunities encountered, and proposes future directions for harnessing AI to enhance Arabic language acquisition and proficiency. This article delves into the existing landscape of AI applications in Arabic language learning. It discusses the utilization of AI-driven techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) in developing intelligent tutoring systems, language learning platforms, and educational games tailored for Arabic learners.

This paper focused on improve results accuracy Alphabet text transliteration into Arabic text in Indonesian Malay. Alphabet text transliteration into Jawi in this research using reference books by Rosadi rule that takes into account the beheading of syllables for determination its Jawi text. The method used in this study to identify syllables is Fine State Automata (FSA) developed by Basuki, however the syllable identifiers can not distinguish beheading diphthong with the vowel sequence, this of course affects the transliteration accuracy. The method used in this study to improve the accuracy of transliteration that modify FSA for identifier Indonesian syllables that have been made by Basuki then added the ability to recognize patterns diphthong syllables and vowel series. Results obtained modifications that have been done syllable recognition accuracy improvement is 25% from the previous. The level of accuracy of the transliteration is 94.74% if calculated based on the results of beheading the right conditions, but when it counted the results obtained from incorrect beheading, the transliteration accuracy is 90%. Keywords: jawi, natural language processing, recognition syllables.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

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.

Social networking sites have become an integral part of everyday life, where people interact, cooperate and quarrel with each other. Social media also encourages them to express their opinions and share their comments about their lives’ events or about the product they use. Opinions can be direct without any comparison (I like ABC phone) or they can be comparative (X-phone’s camera is better than Y-phone). Comparative opinions are useful in many applications, e.g. marketing intelligence, product benchmarking, and e-commerce. The automatic mining of comparative opinions is an important text mining problem and an area of increasing interest for different languages. This paper focuses on identification of comparative sentence from non-comparative ones in Arabic texts. A corpus was developed consisting of YouTube comments. This paper describes research experiments that aimed to apply data/text mining algorithms, natural language processing and linguistic classification for Arabic comparative text discovery. The results of these experiments along with the analysis are also presented. The results were promising reaching to 91% accuracy for the detection of comparative opinions.

Text classification is one of the important applications of Natural Language Processing (NLP) in which text is classified into pre-defined categories or classes. Several state-of-the-arts have already been conducted on text classification in different Indian languages, however, limited researches on text classification performed in Odia. Moreover, the news headlines classification is a text classification technique which is performed in our study mainly focusing on Odia language. The Convolutional Neural Network (CNN) based Deep Learning technique is used to classify the Odia news headlines dataset available at Kaggle repository. The dataset consisting of approx. 15,200 Odia news headlines is split into training and testing sets of 80:20 ratio and is categorized into different classes, such as sports, business, and entertainment by using CNN. The efficacy and efficiency of the proposed CNN-based Odia news headlines classification is evaluated in contrary to other existing machine learning and deep learning models, such as Neural Networks, LSTM (Long Short-Term Memory), and Bi-LSTM (Bidirectional Long Short-Term Memory) with respect to different performance metrics like precision, accuracy, recall, F1-score, training time, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). The simulation results revealed that the proposed CNN model outperformed with respect to other models.

Natural Language Processing (NLP) has witnessed significant advancements in recent years, driven by the emergence of deep learning techniques.Transformers, introduced in 2017, have revolutionized the field of NLP, demonstrating exceptional performance in various tasks.This study aims to explore the potential of Transformers in text classification, a fundamental task in NLP.We conduct a comprehensive evaluation of three pre-trained Transformer models -BERT, RoBERTa, and XLNet -on three benchmark datasets -20 Newsgroups, IMDB, and Stanford Sentiment Treebank.Our results show that Transformers achieve state-of-the-art performance in text classification, outperforming traditional machine learning approaches.We also analyze the strengths and limitations of each model, highlighting their ability to capture long-range dependencies and contextual relationships in text data.Our findings suggest that Transformers are robust and effective models for text classification, with applications in sentiment analysis, spam detection, and information retrieval.We also discuss the potential of Transformers in other NLP tasks, such as question answering, machine translation, and text generation.This study provides a comprehensive overview of the capabilities of Transformers in text classification, highlighting their potential as a powerful tool for NLP tasks.Our results and analysis provide insights for researchers and practitioners working in the field of NLP, highlighting the potential of Transformers for a wide range of applications.

Natural Language Processing (NLP) is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, and text classification and recognition is considered to be an important research field and direction of natural language processing. This paper mainly studies the realization of text classification model through the processing method of text data in natural language processing and the theoretical knowledge and technical means of machine learning. We summarize the existing text classification algorithms, analyze their applicable scenarios, and optimize on the basis of these algorithm models. The paper proposes a Chinese text classification algorithm based on weight preprocessing. Algorithm based on the weight preprocessing link, so that the optimized classifier model can improve the accuracy of the existing text classifier. In this paper, the English Newsgroups corpus is used for experimental verification. The experimental results show that the classification accuracy and accuracy of the improved algorithm are better than those of the traditional text classification algorithm, thereby improving the accuracy of English text classification.

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.

Text classification is a requirement for every text processing application because the web contains a vast amount of text data. Intent detection, information extraction, sentiment analysis, and spam detection involves text categorization. Since text classification uses idioms, metaphors, and polysemic words, intent detection can be difficult. It is challenging to automatically identify idioms in Natural Language Processing applications such as Information Retrieval, Machine Translation, and chatbots. In all these applications, automatic idiom recognition is crucial. In this work, idiomatic and literals sentences are being classified. Idioms are typical expressions with new meanings. This research proposes an ensemble model using pretrained deep learning models to make model with more predictive nature. The models are trained and tested using in-house dataset. Moreover, an in-house dataset that contains 1040 idiomatic and literal sentences is suggested. The experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86% on the test dataset.

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.