# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
###### Abstract
Figure 2: The underlying idea of domain adaptation: (a) a model that performs well in the source domain but worse when tested in the target domain (e.g., a decline in accuracy from 95% to 75%); (b) domain adaptation attempts to train a satisfactory model for the target domain by adding unlabeled target samples to the process (e.g., an improvement in accuracy from 75% to 85%). Over the last decade, many domain adaptation methods have been developed to address the domain dependency problem of sentiment classification and other NLP tasks. The current state of domain adaptation for NLP, including its approaches, applications, and challenges, have recently been surveyed [5]. However, there is a scarcity of research on domain adaptation for Arabic texts in general and on sentiment domain adaptation in particular [6]. Unfortunately, the few studies [6; 7] that have employed domain adaptation for Arabic sentiment classification are hampered by inconsistent comparisons due to them using different datasets and settings. In contrast, all existing studies on English used the well-known Amazon multi-domain dataset [8] in a unified manner (i.e., binary classes, balanced samples and equal size of each domain), which helped provide a broader understanding and accelerate the accumulation of knowledge. Moreover, it is noteworthy that the domain dependency problem is expected to be more severe in Arabic. Aside from the scarcity of Arabic resources, this can be linked to the sparsity of features created by the language's morphological richness, which in turn has a negative impact even on in-domain classification accuracy [9]. However, it remains uncertain how different domain adaptation techniques perform in Arabic when compared with their reported performance in English. This gap calls for further experiments and research. The main objective of the present study is to address the lack of a comparative empirical investigation on domain adaptation techniques for Arabic sentiment classification. We aim to provide a clear understanding of the most effective methods for adapting sentiment classification in Arabic texts by comparing them directly in the same dataset. In addition, we seek to explore the difference in adaptation performance when applied to different types of Arabic texts, namely Modern Standard Arabic (MSA) and Dialectal Arabic (DA), and also when applied to different languages, namely Arabic and English. We achieve these objectives through the following contributions:  
* We employ two public multi-domain sentiment datasets (i.e., MSA and DA) in a unified configuration, following previous research in sentiment domain adaptation in English (i.e., binary classification, balanced dataset and equal domain size), allowing for a consistent comparison of methods and assisting future research;
* We replicate the existing domain adaptation methods for Arabic and compare their performance on both MSA and DA;
* We test two of the best-known domain adaptation methods and assess their performance on Arabic, compared with their reported performance on English. In the present study, we begin by providing a summary of the literature on domain adaptation, outlining the main approaches and listing some examples. Next, we conduct a comparison of two domain adaptation methods for Arabic, namely domain adversarial network with representation learning (DARL) [6] and AraBERT-based adversarial-learned loss for domain adaptation (AraBERT-ALDA) [7], as well as the two well-known adaptation algorithms, domain-adversarial training of neural networks (DANN) [10] and the marginalized stacked denoising autoencoders (mSDA) [11]. We test the effectiveness of these approaches on the large Arabic multi-domain (LAMD) [12] and multi-Domain Arabic resources for sentiment analysis (MARSA) [13] datasets.