A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification

Abstract

Over the last decade, many methods have been developed to address the domain dependency problem of sentiment classification under domain shift. This problem is exacerbated in Arabic by its feature sparsity induced by morphological complexity and dialect variability. However, only a few studies have proposed sentiment domain adaptation methods for Arabic, with inconsistent comparisons resulting from different datasets and settings, making it difficult to identify the most effective approaches. This is the first comparative study of the most effective domain adaptation methods for Arabic sentiment classification. We replicate the existing methods proposed for Arabic and compare their effectiveness on the standard dataset settings. To further examine the extent to which adaptation performance differs between Modern Standard Arabic (MSA) and Dialectal Arabic (DA), we employ two public multi-domain sentiment datasets. We also test two well-established methods that have been thoroughly utilized in English-related studies and examine if they maintain the same levels of performance when applied to Arabic. Our findings indicate that adaptation performanace on MSA is better than on DA for all traditional approaches. However, implementing adaptation on top of transformer-based language models shows superior performance on DA. Finally, methods that have proven to excel in English suffer from low performance when applied to Arabic and exhibit negative transfer in most cases.