  The performance of learning models heavily relies on the availability and
adequacy of training data. To address the dataset adequacy issue, researchers
have extensively explored data augmentation (DA) as a promising approach. DA
generates new data instances through transformations applied to the available
data, thereby increasing dataset size and variability. This approach has
enhanced model performance and accuracy, particularly in addressing class
imbalance problems in classification tasks. However, few studies have explored
DA for the Arabic language, relying on traditional approaches such as
paraphrasing or noising-based techniques. In this paper, we propose a new
Arabic DA method that employs the recent powerful modeling technique, namely
the AraGPT-2, for the augmentation process. The generated sentences are
evaluated in terms of context, semantics, diversity, and novelty using the
Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT
transformer is used on sentiment classification tasks to evaluate the
classification performance of the augmented Arabic dataset. The experiments
were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and
MOVIE. The selected datasets vary in size, label number, and unbalanced
classes. The results show that the proposed methodology enhanced the Arabic
sentiment text classification on all datasets with an increase in F1 score by
4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.
