Abstract: Author profiling is the process of finding characteristics that make up an author’s profile. This paper presents a machine learning-based author profiling model for Arabic users, considering the author’s regional dialect as a crucial characteristic. Various classification algorithms have been implemented: decision tree, KNN, multilayer perceptron, random forest, and support vector machines. A pair of Continuous Bag-of-Word (CBOW) models has been used for word representation. A well-known data set has been used to evaluate the proposed model and a data augmentation process has been implemented to improve the quality of training data. Support vector machines achieved a 50.52% f1-score, outperforming other models.

Ahmed H. AbuElAtta, Mahmoud Sobhy, Ahmed A. El-Sawy and Hamada Nayel, “Arabic Regional Dialect Identification (ARDI) using Pair of Continuous Bag-of-Words and Data Augmentation” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141125

@article{AbuElAtta2023,
title = {Arabic Regional Dialect Identification (ARDI) using Pair of Continuous Bag-of-Words and Data Augmentation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141125},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141125},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Ahmed H. AbuElAtta and Mahmoud Sobhy and Ahmed A. El-Sawy and Hamada Nayel}
}