Search Results (34)

The rapid expansion of mobile government (m-Government) platforms in Saudi Arabia has generated large volumes of user feedback, creating an opportunity for systematic, data-driven evaluation of public digital services. This study conducts a large-scale sentiment analysis of Arabic user reviews collected from five major Saudi m-Government applications, Absher Business, Tawakkalna, Sehhaty, Nusuk, and Najiz. A dataset comprising 84,000 reviews was constructed and classified into positive and negative sentiment categories. Five Arabic transformer-based baseline models, AraBERT, ArabicBERT, CAMeLBERT, SaudiBERT, and MARBERT, were evaluated under a unified experimental framework. Among these, SaudiBERT and MARBERT achieved the strongest performance, with MARBERT obtaining an accuracy of 91.2 percent, an F1-score of 0.858, and an AUC of 0.942. Furthermore, parameter-efficient fine-tuning using QLoRA on MARBERT preserved comparable performance (F1 = 0.854) while substantially reducing computational requirements. These findings demonstrate the feasibility of scalable sentiment analysis for evaluating and improving m-Government services. Full article

The rapid growth of Arabic social media content requires the development of accurate and efficient methods for sentiment analysis. We propose a resource-efficient multi-task learning (MTL) framework for modern standard Arabic (MSA). The model uses a shared AraBERT encoder to jointly predict emotion, polarity, and intention. We integrate knowledge distillation (KD) from a large teacher model, self-distillation (SD) using model self-ensembling, and adversarial training (AT) as a regularization strategy. Experiments conducted on an annotated corpus of MSA tweets demonstrate that all distilled models outperform a fine-tuned multi-task baseline, and the combined KD+SD+AT configuration achieves competitive results. For instance, KD alone raised Macro F1 for emotion from 0.83 to 0.88 and for intention from 0.67 to 0.72. KD+SD+AT achieved the best intention F1 (0.76) and the highest polarity F1 (0.90). Notably, F1-scores for several minority classes show consistent improvement, particularly under KD and combined configurations. Paired t-tests confirm that several improvements, especially those obtained with KD and KD+SD+AT, are statistically significant ($p<0.05$). Our results indicate that distillation, combined with adversarial regularization, enables the development of smaller and more efficient Arabic sentiment models while maintaining competitive accuracy. These findings address a gap in Arabic multi-task sentiment analysis and provide a scalable, resource-efficient framework, along with empirical insights for distillation in Arabic language models. Full article

Sentiment analysis of Arabic text, particularly on social media platforms, presents a formidable set of unique challenges that stem from the language’s complex morphology, its numerous dialectal variations, and the frequent and nuanced use of emojis to convey emotional context. This paper presents SiAraSent, a hybrid framework that integrates traditional text representations, emoji-aware features, and deep contextual embeddings based on Arabic transformers. Starting from a strong and fully interpretable baseline built on Term Frequency–Inverse Definition Frequency (TF–IDF)-weighted character and word N-grams combined with emoji embeddings, we progressively incorporate SinaTools for linguistically informed preprocessing and AraBERT for contextualized encodings. The framework is evaluated on a large-scale dataset of 58,751 Arabic tweets labeled for sentiment polarity. Our design works within four experimental configurations: (1) a baseline traditional machine learning architecture that employs TF-IDF, N-grams, and emoji features with an Support Vector Machine (SVM) classifier; (2) an Large-language Model (LLM) feature extraction approach that leverages deep contextual embeddings from the pre-trained AraBERT model; (3) a novel hybrid fusion model that concatenates traditional morphological features, AraBERT embeddings, and emoji-based features into a high-dimensional vector; and (4) a fully fine-tuned AraBERT model specifically adapted for the sentiment classification task. Our experiments demonstrate the remarkable efficacy of our proposed framework, with the fine-tuned AraBERT architecture achieving an accuracy of 93.45%, a significant 10.89% improvement over the best traditional baseline. Full article

The widespread use of leetspeak and dialectal Arabic on social media poses a critical challenge to automated hate speech detection systems. Existing Arabic NLP models, largely trained on Modern Standard Arabic (MSA), struggle with obfuscated, noisy, and dialect-specific text, leading to poor generalization in real-world scenarios. This study introduces a Hybrid AraBERT–Hierarchical Attention Network (HAN) framework for deobfuscating Iraqi Arabic leetspeak and accurately classifying hate speech. The proposed model employs a custom normalization pipeline that converts digits, symbols, and Latin-script substitutions (e.g., "3يب" → "عيب") into canonical Arabic forms, thereby enhancing tokenization and embedding quality. AraBERT provides deep contextualized representations optimized for Arabic morphology, while HAN hierarchically aggregates and attends to critical words and sentences to improve interpretability and semantic focus. Experimental evaluation on an Iraqi Arabic social media dataset demonstrates that the proposed model achieves 97% accuracy, 96% precision, 96% recall, 96% F1-score, and 0.98 ROC–AUC, outperforming standalone AraBERT and HAN models by up to 6% in F1-score and 4% in AUC. Ablation studies confirm the important role of the normalization stage (F1 = 0.91 without it) and the contribution of hierarchical attention in balancing precision and recall. Robustness testing under controlled perturbations (including character substitutions, symbol obfuscations, typographical noise, and class imbalance) shows performance retention above 91% F1, validating the framework’s noise tolerance and generalization capability. Comparative analysis with state-of-the-art approaches such as DRNNs, arHateDetector, and ensemble BERT systems further highlights the hybrid model’s effectiveness in handling noisy, dialectal, and adversarial text. Full article

Understanding visitor sentiment is essential for developing effective tourism strategies, particularly as Google Maps reviews have become a key channel for public feedback on tourist attractions. Yet, the unstructured format and dialectal diversity of Arabic reviews pose significant challenges for extracting actionable insights at scale. This study evaluates the performance of traditional machine learning and transformer-based models for aspect-based sentiment analysis (ABSA) on Arabic Google Maps reviews of tourist sites across Saudi Arabia. A manually annotated dataset of more than 3500 reviews was constructed to assess model effectiveness across six tourism-related aspects: price, cleanliness, facilities, service, environment, and overall experience. Experimental results demonstrate that multi-head BERT architectures, particularly AraBERT, consistently outperform traditional classifiers in identifying aspect-level sentiment. Ara-BERT achieved an F1-score of 0.97 for the cleanliness aspect, compared with 0.91 for the best-performing classical model (LinearSVC), indicating a substantial improvement. The proposed ABSA framework facilitates automated, fine-grained analysis of visitor perceptions, enabling data-driven decision-making for tourism authorities and contributing to the strategic objectives of Saudi Vision 20300. Full article

Legal judgment prediction is an emerging application of artificial intelligence in the legal domain, offering significant potential to enhance legal decision support systems. Such systems can improve judicial efficiency, reduce burdens on legal professionals, and assist in early-stage case assessment. This study focused on predicting whether a legal case would be Accepted or Rejected using only the Fact section of court rulings. A key challenge lay in processing long legal documents, which often exceeded the input length limitations of transformer-based models. To address this, we proposed a two-step methodology: first, each document was segmented into sentence-level inputs compatible with AraBERT—a pretrained Arabic transformer model—to generate sentence-level predictions; second, these predictions were aggregated to produce a document-level decision using several methods, including Mean, Max, Confidence-Weighted, and Positional aggregation. We evaluated the approach on a dataset of 19,822 real-world cases collected from the Saudi Arabian Commercial Court. Among all aggregation methods, the Confidence-Weighted method applied to the AraBERT-based classifier achieved the highest performance, with an overall accuracy of 85.62%. The results demonstrated that combining sentence-level modeling with effective aggregation methods provides a scalable and accurate solution for Arabic legal judgment prediction, enabling full-length document processing without truncation. Full article

The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) are increasingly strained by the sheer volume in circulation. To combat this issue, machine learning (ML) and natural language processing (NLP) techniques, specifically through transfer learning, are explored to automate Hadith classification into Genuine and Fake categories. This study utilizes an imbalanced dataset of 8544 Hadiths, with 7008 authentic and 1536 fake Hadiths, to systematically investigate the collective impact of both linguistic and contextual features, particularly the chain of narrators (Isnad), on Hadith authentication. For the first time in this specialized domain, state-of-the-art pre-trained language models (PLMs) such as Multilingual BERT (mBERT), CamelBERT, and AraBERT are evaluated alongside classical algorithms like logistic regression (LR) and support vector machine (SVM) for Hadith authentication. Our best-performing model, AraBERT, achieved a 99.94% F1score when including the chain of narrators, demonstrating the profound effectiveness of contextual elements (Isnad) in significantly improving accuracy, providing novel insights into the indispensable role of computational methods in Hadith authentication and reinforcing traditional scholarly emphasis. This research represents a significant advancement in combating misinformation in this important field. Full article

The recent increase in extremist material on social media platforms makes serious countermeasures to international cybersecurity and national security efforts more difficult. RADAR#, a deep ensemble approach for the detection of radicalization in Arabic tweets, is introduced in this paper. Our model combines a hybrid CNN-Bi-LSTM framework with a top Arabic transformer model (AraBERT) through a weighted ensemble strategy. We employ domain-specific Arabic tweet pre-processing techniques and a custom attention layer to better focus on radicalization indicators. Experiments over a 89,816 Arabic tweet dataset indicate that RADAR# reaches 98% accuracy and a 97% F1-score, surpassing advanced approaches. The ensemble strategy is particularly beneficial in handling dialectical variations and context-sensitive words common in Arabic social media updates. We provide a full performance analysis of the model, including ablation studies and attention visualization for better interpretability. Our contribution is useful to the cybersecurity community through an effective early detection mechanism of online radicalization in Arabic language content, which can be potentially applied in counter-terrorism and online content moderation. Full article

Semantic text chunking refers to segmenting text into coherently semantic chunks, i.e., into sets of statements that are semantically related. Semantic chunking is an essential pre-processing step in various NLP tasks e.g., document summarization, sentiment analysis and question answering. In this paper, we propose a hybrid chunking; two-steps semantic text chunking method that combines the effectiveness of unsupervised semantic text chunking based on the similarities between sentences embeddings and the pre-trained language models (PLMs) especially BERT by fine-tuning the BERT on semantic textual similarity task (STS) to provide a flexible and effective semantic text chunking. We evaluated the proposed method in English and Arabic. To the best of our knowledge, there is an absence of an Arabic dataset created to assess semantic text chunking at this level. Therefore, we created an AraWiki50k to evaluate our proposed text chunking method inspired by an existing English dataset. Our experiments showed that exploiting the fine-tuned pre-trained BERT on STS enhances results over unsupervised semantic chunking by an average of 7.4 in the PK metric and by an average of 11.19 in the WindowDiff metric on four English evaluation datasets, and 0.12 in the PK and 2.29 in the WindowDiff for the Arabic dataset. Full article

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning. Full article

Agricultural festivals play a vital role in promoting sustainable farming, local economies, and cultural heritage. Understanding public sentiment toward these events can provide valuable insights to enhance event organization, marketing strategies, and economic sustainability. In this study, we collected and analyzed social media data from Twitter to evaluate public perceptions of Al-Baha’s agricultural festivals. Sentiment analysis was performed using both traditional machine learning and deep learning approaches. Specifically, six machine learning models including Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (KNN), and XGBoost (XGB) were compared against AraBERT, a transformer-based deep learning model. Each model was evaluated based on accuracy, precision, recall, and F1-score. The results demonstrated that AraBERT achieved the highest performance across all metrics, with an accuracy of 85%, confirming its superiority in Arabic sentiment classification. Among traditional models, SVM and RF performed best, whereas MNB and KNN struggled with sentiment detection. These findings highlight the role of sentiment analysis in supporting sustainable agricultural and tourism initiatives. The insights gained from sentiment trends can help festival organizers, policymakers, and agricultural stakeholders make data-driven decisions to enhance sustainable event planning, optimize resource allocation, and improve marketing strategies in line with the Sustainable Development Goals (SDGs). Full article

Background: Pre-Trained Language Models hold significant promise for revolutionizing mental health care by delivering accessible and culturally sensitive resources. Despite this potential, their efficacy in mental health applications, particularly in the Arabic language, remains largely unexplored. To the best of our knowledge, comprehensive studies specifically evaluating the performance of PLMs on diverse Arabic mental health tasks are still scarce. This study aims to bridge this gap by evaluating the performance of pre-trained language models in classifying questions and answers within the mental health care domain. Methods: We used the MentalQA dataset, which comprises Arabic Questions and Answers interactions related to mental health. Our experiments involved four distinct learning strategies: traditional feature extraction, using PLMs as feature extractors, fine-tuning PLMs, and employing prompt-based techniques with models, such as GPT-3.5 and GPT-4 in zero-shot and few-shot learning scenarios. Arabic-specific PLMs, including AraBERT, CAMelBERT, and MARBERT, were evaluated. Results: Traditional feature-extraction methods paired with Support Vector Machines (SVM) showed competitive performance, but PLMs outperformed them due to their superior ability to capture semantic nuances. In particular, MARBERT achieved the highest performance, with Jaccard scores of 0.80 for the question classification and 0.86 for the answer classification. Further analysis revealed that fine-tuning PLMs enhances their performance, and the size of the training dataset plays a critical role in model effectiveness. Prompt-based techniques, particularly few-shot learning with GPT-3.5, demonstrated significant improvements, increasing the accuracy of question classification by 12% and the accuracy of answer classification by 45%. Conclusions: The study demonstrates the potential of PLMs and prompt-based approaches to provide mental health support to Arabic-speaking populations, providing valuable tools for individuals seeking assistance in this field. This research advances the understanding of PLMs in mental health care and emphasizes their potential to improve accessibility and effectiveness in Arabic-speaking contexts. Full article

Mental health disorders represent a substantial global health concern, impacting millions and placing a significant burden on public health systems. Natural Language Processing (NLP) has emerged as a promising tool for analyzing large textual datasets to identify and predict mental health challenges. The aim of this scoping review is to identify the Arabic NLP techniques employed in mental health research, the specific mental health conditions addressed, and the effectiveness of these techniques in detecting and predicting such conditions. This scoping review was conducted according to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) framework. Studies were included if they focused on the application of NLP techniques, addressed mental health issues (e.g., depression, anxiety, suicidal ideation) within Arabic text data, were published in peer-reviewed journals or conference proceedings, and were written in English or Arabic. The relevant literature was identified through a systematic search of four databases: PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. The results of the included studies revealed a variety of NLP techniques used to address specific mental health issues among Arabic-speaking populations. Commonly utilized techniques included Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Recurrent Neural Network (RNN), and advanced transformer-based models such as AraBERT and MARBERT. The studies predominantly focused on detecting and predicting symptoms of depression and suicidality from Arabic social media data. The effectiveness of these techniques varied, with trans-former-based models like AraBERT and MARBERT demonstrating superior performance, achieving accuracy rates of up to 99.3% and 98.3%, respectively. Traditional machine learning models and RNNs also showed promise but generally lagged in accuracy and depth of insight compared to transformer models. This scoping review highlights the significant potential of NLP techniques, particularly advanced transformer-based models, in addressing mental health issues among Arabic-speaking populations. Ongoing research is essential to keep pace with the rapidly evolving field and to validate current findings. Full article

Paraphrasing means expressing the semantic meaning of a text using different words. Paraphrasing has a significant impact on numerous Natural Language Processing (NLP) applications, such as Machine Translation (MT) and Question Answering (QA). Machine Learning (ML) methods are frequently employed to generate new paraphrased text, and the generative method is commonly used for text generation. Generative Pre-trained Transformer (GPT) models have demonstrated effectiveness in various text generation tasks, including summarization, proofreading, and rephrasing of English texts. However, GPT-4’s capabilities in Arabic paraphrase generation have not been extensively studied despite Arabic being one of the most widely spoken languages. In this paper, the researchers evaluate the capabilities of GPT-4 in text paraphrasing for Arabic. Furthermore, the paper presents a comprehensive evaluation method for paraphrase quality and developing a detailed framework for evaluation. The framework comprises Bilingual Evaluation Understudy (BLEU), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Lexical Diversity (LD), Jaccard similarity, and word embedding using the Arabic Bi-directional Encoder Representation from Transformers (AraBERT) model with cosine and Euclidean similarity. This paper illustrates that GPT-4 can effectively produce a new paraphrased sentence that is semantically equivalent to the original sentence, and the quality framework efficiently ranks paraphrased pairs according to quality criteria. Full article

Road traffic accidents (RTAs) are a significant public health and safety concern, resulting in numerous injuries and fatalities. The growing number of cases referred to traffic accident rooms in courts has underscored the necessity for an automated solution to determine victim indemnifications, particularly given the limited number of specialized judges and the complexity of cases involving multiple victims. This paper introduces RTACompensator, an artificial intelligence (AI)-driven decision support system designed to automate indemnification calculations for road accident victims. The system comprises two main components: a calculation module that determines initial compensation based on factors such as age, salary, and medical assessments, and a machine learning (ML) model that assigns liability based on police accident reports. The model uses Arabic bidirectional encoder representations from transformer (AraBERT) embeddings to generate contextual vectors from the report, which are then processed by extreme gradient boosting (XGBoost) to determine responsibility. The model was trained on a purpose-built Arabic corpus derived from real-world legal judgments. To expand the dataset, two data augmentation techniques were employed: multilingual bidirectional encoder representations from transformers (BERT) and Gemini, developed by Google DeepMind. Experimental results demonstrate the model’s effectiveness, achieving accuracy scores of 97% for the BERT-augmented corpus and 97.3% for the Gemini-augmented corpus. These results underscore the system’s potential to improve decision-making in road accident indemnifications. Additionally, the constructed corpus provides a valuable resource for further research in this domain, laying the groundwork for future advancements in automating and refining the indemnification process. Full article