# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 2 Literature Review
Examples of the weighting and selection approach include positive and unlabeled learning [27] and principal component analysis-based sample selection [28]. In the self-labeling approach, source-labeled data are used to train a model, which is then applied to iteratively predict target pseudo-labels [29]. Moving the training data gradually from the source to the target domain is the idea underlying self-labeling methods. In the first work to employ self-labeling for domain adaptation, expectation-maximization was used to adjust a naive Bayes classifier [30]. The classical self-labeling algorithms of self-training [31], co-training [32] and tri-training [33] were used in later investigations. To increase the duration and spatial complexity of the initial tri-training strategy, a recent study proposed multi-task tri-training [34]. Other, more recent efforts [35; 36] have integrated instance-based self-labeling and feature-based representational learning techniques. Traditional graph-based, semi-supervised algorithms, such as RANK [37] and label propagation [38], also provide a method for self-labeling to transfer sentiment between domains. When it comes to learning representations, instance-based deep learning techniques are similar to feature-based ones. However, they vary in that they use domain-adversarial loss rather than autoencoders to reduce domain discrepancies [5]. DANN [10], the earliest and most popular domain-adversarial technique, learns a representation by maximizing the domain classifier's loss while minimizing that of the source sentiment classifier. By using two memory networks for sentiment and domain, the adversarial memory network [39] combines DANN and the pivot-based approach. Recently, finetuning pretrained LMs on source-labeled samples has been a straightforward adaptation approach [5]. Although it reveals robust cross-domain performance and sometimes surpasses conventional domain adaptation methods, it still suffers from various degrees of performance decline. Thus, a later work proposed adversarial adaptation with distillation (AAD) [40] as a special adversarial domain adaptation method for pretrained LMs. After finetuning a BERT model on the source samples, AAD uses the weights obtained to initialize another BERT model for the target domain and apply both adversarial learning and knowledge distillation. The study found that knowledge distillation (i.e., transferring knowledge from large models to smaller ones) functions as a type of regularization that eases model overfitting on the source samples. Moreover, a new line of approach has emerged that leverages contrastive learning (CL), a type of self-supervised representation learning based on data augmentation. Domain confused contrastive learning (DCCL) [41], for example, utilizes adversarial attack with perturbations to create domain-confused samples from the source and target domains. Next, CL brings the original samples from both domains closer to their domain-confused counterparts; in other words, DCCL gradually pulls samples to the domain decision boundary and hence reduces domain discrepancy. Using a different strategy, contrastive transformer-based domain adaptation [42] trains an adaptor to derive domain-shared features by minimizing the distance between domains and a CL-based discriminator to maintain the domain-specific features. Domain adaptation for Arabic sentiment classification began with the first work, DARL [6], which combines adversarial learning and the denoising reconstruction paradigm. The results on the ArSentD-LEV multi-domain dataset [43] demonstrated that combining reconstruction loss with adversarial training improved performance somewhat in some circumstances. However, a basic support vector machine (SVM) classifier produced competitive results in some experiments. Furthermore, the paper was the first to put DANN to the test on an Arabic dataset.