Abstract
Recently Natural Language Processing (NLP) has excessive attention due to increased data available online and needs processing. Nevertheless, the huge development in the NLP but still Arabic Natural Language Processing (ANLP) faces many challenges and grief from researchers’ leakage compared with English NLP. Mainly this study has three divisions. It aims to find out the challenges facing ANLP, especially text classification.
Furthermore, it aims to examine the effect of using deep learning in ANLP text classification from both sides’ advantages and disadvantages. Moreover, it aims to find the most efficient deep learning algorithm that sufficiently classifies and categorizes an Arabic text. To fulfill the objectives of this study, three different methods have been used. The first method, Survey / Systematic Review, to find out the challenges. The second method, Literature review; to examine the effect of using deep learning algorithms to classify Arabic text. The third method, experiment to find an efficient algorithm that reverts better accuracy. In this study, we have determined the data collection methods. Where searching for good quality research papers is the key to accomplish the first two divisions. But the last division needs the full implementation to deploy Arabic text classification models. The Data Analysis explained the sub-steps extensively in each division, how it will be implemented, and each division’s expected outcome. Also, this study briefly gives the resources needed and the required timeline. Deploying the models will be the longest process; hence it needs preprocessing and building models covering all types of neural networks to determine the most efficient one.
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References
Salloum, S.A., AlHamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining, vol. 740 (2018)
Or, C.K.L., Karsh, B.-T.: A systematic review of patient acceptance of consumer health information technology. J. Am. Med. Inform. Assoc. 16(4), 550–560 ( 2009)
Alshurideh, M., Al Kurdi, B., Salloum, S.A., Arpaci, I., Al-Emran, M.: Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interact. Learn. Environ. 1–15 (2020)
Salloum, S.A., Al-Emran, M., Monem, A., Shaalan, K.: A survey of text mining in social media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. Syst. J. 2(1), 127–133 (2017)
Almazrouei, F.A., Alshurideh, M., Al Kurdi, B., Salloum, S.A.: Social media impact on business: a systematic review. AISC, vol. 1261 (2021)
Shaalan, K., Al Mansoori, S., Salloum, S.A.: The impact of artificial intelligence and information technologies on the efficiency of knowledge management at modern organizations: a systematic review. In: Al-Emran, M., Shaalan, K., Hassanien, A. (eds.) Recent Advances in Intelligent Systems and Smart Applications. Studies in Systems, Decision and Control, vol. 295. Springer, Cham (2021)
AlShamsi, M., Salloum, S.A., Alshurideh, M., Abdallah, S.: Artificial intelligence and blockchain for transparency in governance. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds.) Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, pp. 219–230. Springer, Cham (2021)
AlGhanem, H., Shanaa, M., Salloum, S., Shaalan, K.: The role of KM in enhancing AI algorithms and systems. Adv. Sci. Technol. Eng. Syst. J. 5(4), 388–396 (2020)
Yousuf, H., Salloum, S.: Survey analysis: enhancing the security of vectorization by using word2vec and CryptDB. Adv. Sci. Technol. Eng. Syst. J. 5(4), 374–380 (2020)
Yousuf, H., Zainal, A.Y., Alshurideh, M., Salloum, S.A.: Artificial intelligence models in power system analysis. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds.) Artificial Intelligence for Sustainable Development: Theory, Practice and Future Applications, pp. 231–242. Springer, Cham (2021)
Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for extracting information from research articles, vol. 740 (2018)
Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining social media text: extracting knowledge from Facebook. Int. J. Comput. Digit. Syst. 6(2), 73–81 (2017)
Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab Gulf newspapers using text mining techniques. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 396–405 (2017)
Salloum, S.A., Alshurideh, M., Elnagar, A., Shaalan, K.: Mining in educational data: review and future directions. In: Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 92–102 (2020)
Etaiwi, W., Awajan, A.: Graph-based Arabic NLP techniques: a survey. Procedia Comput. Sci. 142, 328–333 (2018)
Wahdan, K.S.A., Hantoobi, S., Salloum, S.A., Shaalan, K.: A systematic review of text classification research based ondeep learning models in Arabic language. Int. J. Electr. Comput. Eng. 10(6), 6629–6643 (2020)
Elnagar, A., Al-Debsi, R., Einea, O.: Arabic text classification using deep learning models. Inf. Process. Manag. 57(1), 102121 (2020)
Bahassine, S., Madani, A., Kissi, M.: An improved Chi-sqaure feature selection for Arabic text classification using decision tree. In: SITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications (2016)
Mohammad, A.H., Alwada’n, T., Almomani, O.: Arabic text categorization using support vector machine, Nave Bayes and neural network. Glob. Sci. Technol. Forum J. Comput. 5(1), 108–115 (2016)
Elhassan, R., Ahmed, M.: Arabic text classification review. Int. J. Comput. Sci. Softw. Eng. (IJCSSE) 4(1), 1–5 (2015). ISSN (Online): 2409-4285. www.IJCSSE.org
Abdeen, M.A.R., AlBouq, S., Elmahalawy, A., Shehata, S.: A closer look at arabic text classification. Int. J. Adv. Comput. Sci. Appl. 10(11), 677–688 (2019)
Zhong, B., Xing, X., Love, P., Wang, X., Luo, H.: Convolutional neural network: deep learning-based classification of building quality problems. Adv. Eng. Inform. 40, 46–57 (2019)
Boukil, S., Biniz, M., El Adnani, F., Cherrat, L., El Moutaouakkil, A.E.: Arabic text classification using deep learning technics. Int. J. Grid Distrib. Comput. 11(9), 103–114 (2018)
El-Alami, F.-Z., Ouatik, S., Alaoui, E.: An efficient method based on deep learning approach for Arabic text categorization. In: International Arab Conference on Information Technology (2016)
Jindal, V.: A personalized markov clustering and deep learning approach for Arabic text categorization. In: 54th Annual Meeting of the Association for Computational Linguistics ACL 2016 - Student Research Workshop, pp. 145–151 (2016)
Cavalli-Sforza, V., Saddiki, H., Nassiri, N.: Arabic readability research: current state and future directions. Procedia Comput. Sci. 142, 38–49 (2018)
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This work is a part of a project undertaken at the British University in Dubai.
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Wahdan, A., Salloum, S.A., Shaalan, K. (2021). Text Classification of Arabic Text: Deep Learning in ANLP. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_10
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DOI: https://doi.org/10.1007/978-3-030-69717-4_10
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