  In this paper, we conduct an in-depth analysis of several key factors
influencing the performance of Arabic Dialect Identification NADI'2023, with a
specific focus on the first subtask involving country-level dialect
identification. Our investigation encompasses the effects of surface
preprocessing, morphological preprocessing, FastText vector model, and the
weighted concatenation of TF-IDF features. For classification purposes, we
employ the Linear Support Vector Classification (LSVC) model. During the
evaluation phase, our system demonstrates noteworthy results, achieving an F1
score of 62.51%. This achievement closely aligns with the average F1 scores
attained by other systems submitted for the first subtask, which stands at
72.91%.
