## 1 Introduction  
Recent years have witnessed a proliferation of deep learning methods that significantly enhance various machine learning problems, especially in Natural Language Processing (NLP) tasks such as sentiment analysis [1, 2]. While conventional deep learning stands as the primary and impactful technique for sentiment analysis [3], its effectiveness relies on the assumption that the distributions of training and testing data share commonalities and originate from the same source. Unfortunately, this assumption often proves incorrect, leading to divergence in the distribution and characteristics of training and testing data sets. This divergence stems from factors such as the (i) prior shift, (ii) co-variate shift, and (iii) concept shift [4, 5]. In the context of sentiment analysis, a tangible example of prior shift could be observed in the evaluation of a product's reviews concerning its price before and after a sale. This shift in prior probabilities can significantly impact the interpretation of sentiment, as the change in context may lead to different sentiments being expressed by reviewers based on the altered pricing conditions. On the other hand, the co-variate shift emerges when sample selection bias impacts proper generalization from the learned model. This issue arises when the training data lacks adequate randomization, resulting in an unrepresentative sample. For instance, bias could occur by only selecting either low or high-rated reviews in sentiment analysis. Finally, concept shift occurs when data properties, like variance, mean, and co-variance, change over time. This is evident when sentiment expressed in social media posts evolves or fluctuates over time. This poses a challenge for sentiment analysis models that rely on historical data, as the underlying patterns and relationships may no longer accurately represent the current state of sentiment. Instances of these shifts create distinct distributions between the source and target data sets, known as domain shift. This domain shift significantly diminishing the efficacy of conventional statistical learning techniques, highlighting the importance of acquiring labeled data sets that accurately represent the new distribution. However, the challenge lies in the cost and time constraints associated with collecting these new data sets, which must faithfully capture the updated distribution [6, 7]. In tackling this challenge, researchers have explored different approaches and techniques with the goal of enhancing the efficiency of learning models by addressing variations in data distributions across training and testing domains [5, 6]. One suggested solution involves alleviating domain disparities across domains through the implementation of what is known as domain adaptation techniques. Domain adaptation (DA) entails the identification of shared-features and the minimization of discrepancies in feature distributions across different domains [5]. In the context of sentiment analysis or other natural language processing tasks, domains may represent different sources of text data with distinct characteristics, such as different vocabulary, or subject. There are three main categories of domain adaptation, distinguished by the availability of labeled data: (i) supervised domain adaptation (SDA), (ii) semi-supervised domain adaptation (SSDA), and (iii) unsupervised domain adaptation (UDA). SDA involves labeled data in both the source and target domains which assumes labeled data exists in both the source and target domains, allowing for fine-tuning of the model [8]. SSDA deals with a limited amount of labeled data in the target domain, and the model is trained using both labeled data from the source domain and the available labeled data from the target domain [6, 9]. UDA assumes no labeled data in the target domain, requiring unsupervised techniques to align feature distributions between the source and target domains [4, 10, 11]. UDA has gained significant importance in machine learning research [2, 12, 13].