Abstract
There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jackson, P., & Moulinier, I. (2007). Natural language processing for online applications: text retrieval, extraction and categorization (vol. 5). John Benjamins Publishing.
Sanasam, R., Murthy, H., & Gonsalves, T. (2010). Feature selection for text classification based on Gini coefficient of inequality. FSDM, 10, 76–85.
Feldman, R. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval.
Gharaibeh, M., Alzu’bi, D., Abdullah, M., Hmeidi, I., Al Nasar, M. R., Abualigah, L., & Gandomi, A. H. (2022). Radiology imaging scans for early diagnosis of kidney tumors: a review of data analytics-based machine learning and deep learning approaches. Big Data and Cognitive Computing, 6(1), 29.
Gandomi, A. H., Chen, F., & Abualigah, L. (2022). Machine learning technologies for big data analytics. Electronics, 11(3), 421.
Bashabsheh, M. Q., Abualigah, L., & Alshinwan, M. (2022). Big data analysis using hybrid meta-heuristic optimization algorithm and MapReduce framework. In Integrating meta-heuristics and machine learning for real-world optimization problems (pp. 181–223). Springer.
Gharaibeh, M., Almahmoud, M., Ali, M. Z., Al-Badarneh, A., El-Heis, M., Abualigah, L., Altalhi, M., Alaiad, A., & Gandomi, A. H. (2021). Early diagnosis of alzheimer’s disease using cerebral catheter angiogram neuroimaging: A novel model based on deep learning approaches. Big Data and Cognitive Computing, 6(1), 2.
Abualigah, L., Diabat, A., & Elaziz, M. A. (2021). Intelligent workflow scheduling for big data applications in IoT cloud computing environments. Cluster Computing, 24(4), 2957–2976.
Abualigah, L., Gandomi, A. H., Elaziz, M. A., Hamad, H. A., Omari, M., Alshinwan, M., & Khasawneh, A. M. (2021). Advances in meta-heuristic optimization algorithms in big data text clustering. Electronics, 10(2), 101.
Abualigah, L., & Masri, B. A. (2021). Advances in MapReduce big data processing: platform, tools, and algorithms. In Artificial intelligence and IoT (pp. 105–128).
Al-Sai, Z. A., & Abualigah, L. M. (2017, May). Big data and e-government: A review. In 2017 8th international conference on information technology (ICIT) (pp. 580–587). IEEE.
Alshaer, H., Otair, M., Abualigah, L., Alshinwan, M., & Khasawneh, A. (2020). Feature selection method using improved CHI Square on Arabic text classifiers.
Chantar, H., Mafarja, M., Alsawalqah, H., Heidari, A. A., Aljarah, I., & Faris, H. (2020). Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification.
Bahassine, S., Madani, A., Al-Sarem, M., & Kissi, M. (2020). Feature selection using an improved Chi-square for Arabic text.
Marie-Sainte, S. L., & Alalyani, N. (2020). Firefly algorithm based feature selection for Arabic text classification.
Elnagar, A., Al-Debsi, R., & Einea, O. (2020). Arabic text classification using deep learning models.
Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.
Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.
Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158.
Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570.
Oyelade, O. N., Ezugwu, A. E. S., Mohamed, T. I., & Abualigah, L. (2022). Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm. IEEE Access, 10, 16150–16177.
Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 1–49.
Khreisat, L. (2009). A machine learning approach for Arabic text classification using N-gram frequency statistics. Journal of Informetrics, 72–77.
Sebastiani, F. (2005). Text categorization. In J. H. Doorn, L. C. Rivero, & V. E. Ferraggine (Eds.), Encyclopedia of database technologies and applications (pp. 683–687). IGI Global.
Dharmadhikari, S., Ingle, M., & Kulkarni, P. (2011). Empirical studies on machine learning based text classification algorithms. Advanced Computing: An International Journal, 161–169.
El Kourdi, M., Bensaid, A., & Rachidi, T. (2004). Automatic Arabic document categorization based on the Naïve Bayes algorithm. In Proceedings of the workshop on computational approaches to Arabic script-based languages (pp. 51–58).
Elnagar, A., Al-Debsi, R., & Einea, O. (2020). Arabic text classification using deep learning models. Information Processing and Management.
Mirjalili, S., Mirjalili, S. M., & Lewisa, A. (2014). Grey Wolf optimizer. Advances in Engineering Software.
Sayadi, M. K., Ramezanian, R., & Ghaffarinasab, N. (2010). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations.
Harrag, A., & Nassir, H. (2014). Firefly feature subset selection application to Arabic speaker recognition system. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Melhem, M.K.B., Abualigah, L., Zitar, R.A., Hussien, A.G., Oliva, D. (2023). Comparative Study on Arabic Text Classification: Challenges and Opportunities. In: Abualigah, L. (eds) Classification Applications with Deep Learning and Machine Learning Technologies. Studies in Computational Intelligence, vol 1071. Springer, Cham. https://doi.org/10.1007/978-3-031-17576-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-17576-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17575-6
Online ISBN: 978-3-031-17576-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)