{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:28:25Z","timestamp":1768559305705,"version":"3.49.0"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["IITP-2021-2017-0-01629"],"award-info":[{"award-number":["IITP-2021-2017-0-01629"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korea Government (MSIT)","award":["2017-0-00655"],"award-info":[{"award-number":["2017-0-00655"]}]},{"name":"Institute for Information &amp; Communications Technology Promotion (IITP)","award":["IITP-2021-2020-0-01489"],"award-info":[{"award-number":["IITP-2021-2020-0-01489"]}]},{"DOI":"10.13039\/501100003725","name":"NRF","doi-asserted-by":"publisher","award":["NRF-2019R1A2C2090504"],"award-info":[{"award-number":["NRF-2019R1A2C2090504"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Arabic text classification is a process to simultaneously categorize the different contextual Arabic contents into a proper category. In this paper, a novel deep learning Arabic text computer-aided recognition (ArCAR) is proposed to represent and recognize Arabic text at the character level. The input Arabic text is quantized in the form of 1D vectors for each Arabic character to represent a 2D array for the ArCAR system. The ArCAR system is validated over 5-fold cross-validation tests for two applications: Arabic text document classification and Arabic sentiment analysis. For document classification, the ArCAR system achieves the best performance using the Alarabiya-balance dataset in terms of overall accuracy, recall, precision, and F1-score by 97.76%, 94.08%, 94.16%, and 94.09%, respectively. Meanwhile, the ArCAR performs well for Arabic sentiment analysis, achieving the best performance using the hotel Arabic reviews dataset (HARD) balance dataset in terms of overall accuracy and F1-score by 93.58% and 93.23%, respectively. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, understanding, and classification.<\/jats:p>","DOI":"10.3390\/a14070216","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T10:52:58Z","timestamp":1626432778000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-9261","authenticated-orcid":false,"given":"Abdullah Y.","family":"Muaad","sequence":"first","affiliation":[{"name":"Department of Studies in Computer Science, Mysore University, Manasagangothri, Mysore 570006, India"},{"name":"Sana\u2019a Community College, Sana\u2019a 5695, Yemen"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6311-5575","authenticated-orcid":false,"given":"Hanumanthappa","family":"Jayappa","sequence":"additional","affiliation":[{"name":"Department of Studies in Computer Science, Mysore University, Manasagangothri, Mysore 570006, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4457-4407","authenticated-orcid":false,"given":"Mugahed A.","family":"Al-antari","sequence":"additional","affiliation":[{"name":"Sana\u2019a Community College, Sana\u2019a 5695, Yemen"},{"name":"Department of Computer Science and Engineering, College of Software, Kyung Hee University, Suwon-si 17104, Gyeonggi-do, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-1587","authenticated-orcid":false,"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Software, Kyung Hee University, Suwon-si 17104, Gyeonggi-do, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., and Zhai, C. (2012). A survey of text classification algorithms. Mining Text Data, Springer.","DOI":"10.1007\/978-1-4614-3223-4"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3390092","article-title":"Robust Arabic Text Categorization by Combining Convolutional and Recurrent Neural Networks","volume":"19","author":"Ameur","year":"2020","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP)"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Larkey, L.S., and Connell, M.E. (2001, January 13\u201316). Arabic information retrieval at UMass in TREC-10. Proceedings of the Tenth Text Retrieval Conference, Gaithersburg, MD, USA.","DOI":"10.6028\/NIST.SP.500-250.umass"},{"key":"ref_4","first-page":"130","article-title":"Translating Ambiguous Arabic Words Using Text Mining","volume":"8","author":"Mohammed","year":"2019","journal-title":"Int. J. Comput. Sci. Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.ipm.2017.08.003","article-title":"Machine translation for Arabic dialects (survey)","volume":"56","author":"Harrat","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_6","first-page":"1","article-title":"Filtering Spam E-Mail from Mixed Arabic and English Messages: A Comparison of Machine Learning Techniques","volume":"6","year":"2009","journal-title":"Int. Arab. J. Inf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shehab, M.A., Badarneh, O., Al-Ayyoub, M., and Jararweh, Y. (2016, January 13\u201314). A supervised approach for multi-label classification of Arabic news articles. Proceedings of the 2016 7th International Conference on Computer Science and Information Technology (CSIT), Amman, Jordan.","DOI":"10.1109\/CSIT.2016.7549465"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ipm.2017.08.004","article-title":"Approaches for preserving content integrity of sensitive online Arabic content: A survey and research challenges","volume":"56","author":"Hakak","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102121","DOI":"10.1016\/j.ipm.2019.102121","article-title":"Arabic text classification using deep learning models","volume":"57","author":"Elnagar","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8079","DOI":"10.1007\/s13369-018-3286-z","article-title":"An enhanced latent semantic analysis approach for arabic document summarization","volume":"43","author":"Zhang","year":"2018","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_11","first-page":"11","article-title":"Arabic language: Characteristics and importance","volume":"1","author":"Hasanuzzaman","year":"2013","journal-title":"Echo J. Humanit. Soc. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"511","DOI":"10.18517\/ijaseit.7.2.1810","article-title":"A comparative review of machine learning for Arabic named entity recognition","volume":"7","author":"Salah","year":"2017","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_13","first-page":"7011","article-title":"Arabic natural language processing and machine learning-based systems","volume":"7","author":"Alalyani","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102124","DOI":"10.1016\/j.ipm.2019.102124","article-title":"Building a morpho-semantic knowledge graph for Arabic information retrieval","volume":"57","author":"Bounhas","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., and Brown, D. (2019). Text classification algorithms: A survey. Information, 10.","DOI":"10.3390\/info10040150"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.ipm.2018.01.006","article-title":"Enhancing aspect-based sentiment analysis of Arabic hotels\u2019 reviews using morphological, syntactic and semantic features","volume":"56","author":"Jararweh","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_17","first-page":"395","article-title":"Denoising images of dual energy X-ray absorptiometry using non-local means filters","volume":"26","author":"Metwally","year":"2018","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Conneau, A., Schwenk, H., Barrault, L., and Lecun, Y. (2016). Very deep convolutional networks for text classification. arXiv.","DOI":"10.18653\/v1\/E17-1104"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Duque, A.B., Santos, L.L.J., Mac\u00eado, D., and Zanchettin, C. (2019, January 17\u201319). Squeezed Very Deep Convolutional Neural Networks for Text Classification. Proceedings of the International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30487-4_16"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Daif, M., Kitada, S., and Iyatomi, H. (2020). AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss. arXiv.","DOI":"10.18653\/v1\/2020.acl-srw.29"},{"key":"ref_21","unstructured":"Zhang, X., and LeCun, Y. (2015). Text understanding from scratch. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"104076","DOI":"10.1016\/j.dib.2019.104076","article-title":"Sanad: Single-label arabic news articles dataset for automatic text categorization","volume":"25","author":"Einea","year":"2019","journal-title":"Data Brief"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","article-title":"Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks","volume":"162","author":"Choi","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cmpb.2018.01.017","article-title":"Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system","volume":"157","author":"Park","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","article-title":"A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification","volume":"117","author":"Choi","year":"2018","journal-title":"Int. J. Med Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105584","DOI":"10.1016\/j.cmpb.2020.105584","article-title":"Evaluation of Deep Learning Detection and Classification towards Computer-aided Diagnosis of Breast Lesions in Digital X-ray Mammograms","volume":"196","author":"Kim","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_27","first-page":"2890","article-title":"Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images","volume":"51","author":"Hua","year":"2020","journal-title":"Appl. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gamb\u00e4ck, B., and Sikdar, U.K. (2017, January 4). Using convolutional neural networks to classify hate-speech. Proceedings of the First Workshop on Abusive Language Online, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/W17-3013"},{"key":"ref_30","first-page":"1","article-title":"Introduction to Arabic natural language processing","volume":"3","author":"Habash","year":"2010","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10462-016-9508-4","article-title":"Multilingual sentiment analysis: From formal to informal and scarce resource languages","volume":"48","author":"Lo","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cambria, E., Das, D., Bandyopadhyay, S., and Feraco, A. (2017). Affective computing and sentiment analysis. A Practical Guide to Sentiment Analysis, Springer.","DOI":"10.1007\/978-3-319-55394-8"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.ipm.2017.09.005","article-title":"Writer identification approach based on bag of words with OBI features","volume":"56","author":"Durou","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"El Kourdi, M., Bensaid, A., and Rachidi, T.-E. (2004, January 28). Automatic Arabic document categorization based on the Na\u00efve Bayes algorithm. Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages, Geneva, Switzerland.","DOI":"10.3115\/1621804.1621819"},{"key":"ref_35","unstructured":"Al-Harbi, S., Almuhareb, A., Al-Thubaity, A., Khorsheed, M., and Al-Rajeh, A. Automatic Arabic Text Classification; In Proceedings of the 9th International Conference on the Statistical Analysis of Textual Data, Lyon, France, 12\u201314 March 2008."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102183","DOI":"10.1016\/j.ipm.2019.102183","article-title":"Graph-based Arabic text semantic representation","volume":"57","author":"Etaiwi","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.procs.2017.08.363","article-title":"The use of hidden Markov model in natural arabic language processing: A survey","volume":"113","author":"Suleiman","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ipm.2018.07.006","article-title":"A comprehensive survey of arabic sentiment analysis","volume":"56","author":"Khamaiseh","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.14257\/ijgdc.2018.11.9.09","article-title":"Arabic text classification using deep learning technics","volume":"11","author":"Boukil","year":"2018","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127913","DOI":"10.1109\/ACCESS.2020.3009217","article-title":"Impact of stemming and word embedding on deep learning-based arabic text categorization","volume":"8","author":"Almuzaini","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.ipm.2017.07.004","article-title":"Modeling arabic subjectivity and sentiment in lexical space","volume":"56","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.future.2020.05.034","article-title":"A review of sentiment analysis research in Arabic language","volume":"112","author":"Oueslati","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kim, Y., Jernite, Y., Sontag, D., and Rush, A.M. (2015). Character-aware neural language models. arXiv.","DOI":"10.1609\/aaai.v30i1.10362"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.ipm.2017.07.003","article-title":"Language processing and learning models for community question answering in arabic","volume":"56","author":"Romeo","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alayba, A.M., Palade, V., England, M., and Iqbal, R. (2018, January 12\u201314). Improving sentiment analysis in Arabic using word representation. Proceedings of the 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), London, UK.","DOI":"10.1109\/ASAR.2018.8480191"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Al-Taani, A.T., and Al-Sayadi, S.H. (2020). Classification of Arabic Text Using Singular Value Decomposition and Fuzzy C-Means Algorithms. Applications of Machine Learning, Springer.","DOI":"10.1007\/978-981-15-3357-0_8"},{"key":"ref_48","first-page":"395","article-title":"Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text","volume":"30","author":"Elfaik","year":"2020","journal-title":"J. Intell. Syst."},{"key":"ref_49","first-page":"381","article-title":"A deep autoencoder-based representation for arabic text categorization","volume":"19","year":"2020","journal-title":"J. Inf. Commun. Technol."},{"key":"ref_50","first-page":"25","article-title":"Arabic Opinion Mining Using Combined CNN-LSTM Models","volume":"12","author":"Elzayady","year":"2020","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_51","first-page":"649","article-title":"Character-level convolutional networks for text classification","volume":"28","author":"Zhang","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_52","unstructured":"Saad, M.K., and Ashour, W.M. (2010, January 25\u201326). Osac: Open Source Arabic Corpora. Proceedings of the 6th International Conference on Electrical and Computer Systems (EECS\u201910), Lefke, North Cyprus."},{"key":"ref_53","unstructured":"Chowdhury, S.A., Abdelali, A., Darwish, K., Soon-Gyo, J., Salminen, J., and Jansen, B.J. (2020, January 1). Improving Arabic Text Categorization Using Transformer Training Diversification. Proceedings of the Fifth Arabic Natural Language Processing Workshop, Barcelona, Spain."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Elnagar, A., and Einea, O. (December, January 29). Brad 1.0: Book reviews in arabic dataset. Proceedings of the 2016 IEEE\/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir, Morocco.","DOI":"10.1109\/AICCSA.2016.7945800"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Elnagar, A., Khalifa, Y.S., and Einea, A. (2018). Hotel Arabic-reviews dataset construction for sentiment analysis applications. Intelligent Natural Language Processing: Trends and Applications, Springer.","DOI":"10.1007\/978-3-319-67056-0_3"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ipm.2017.07.002","article-title":"Improved Arabic speech recognition system through the automatic generation of fine-grained phonetic transcriptions","volume":"56","author":"Alsharhan","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"102438","DOI":"10.1016\/j.ipm.2020.102438","article-title":"A comparative study of effective approaches for Arabic sentiment analysis","volume":"58","author":"Farha","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_58","first-page":"539","article-title":"A Study on Deep Learning Binary Classification of Prostate Pathological Images Using Multiple Image Enhancement Techniques","volume":"23","author":"Park","year":"2020","journal-title":"J. Korea Multimed. Soc."},{"key":"ref_59","unstructured":"Agarap, A.F. (2018). Deep Learning using Rectified Linear Units (ReLU). arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/7\/216\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:59Z","timestamp":1760164259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/7\/216"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":60,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["a14070216"],"URL":"https:\/\/doi.org\/10.3390\/a14070216","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]}}}