## 1 Introduction
This is primarily because UDA alleviates the requirement for labeled data in the target domain, which might be inaccessible or come with high costs. This quality renders UDA an invaluable approach, allowing models to adapt to new domains [14]. Acknowledging its significance and wide-ranging applicability, this paper will focus on the exploration and investigation of UDA techniques. Unsupervised Domain Adaptation (UDA) is geared towards transferring knowledge from a labeled domain (source domain) to an unlabeled domain (target domain) to enhance performance in the latter [4, 15]. Various techniques, including domain adversarial learning, have been employed to align feature representations [16, 17], collectively aiming to facilitate effective model adaptation. Adversarial-based UDA approach involves training a domain classifier to distinguish between source and target domains while simultaneously training a sentiment classifier to predict sentiment labels. The goal is to learn domain-invariant features that are informative for sentiment analysis [18]. However, the challenge arises when the target domain has limited resources, making it uncertain that the learned domain-invariant representation contains sufficient relevant features. This issue becomes more pronounced when there is significant dissimilarity between the source and target domains, potentially leading to unstable training. To address this concern and propose a pragmatic UDA approach, we present the Weighted Sequential UDA framework (WS-UDA). WS-UDA aims to enhance the understanding of the target domain and elevate target performance through a weighted sequential framework. This training approach unfolds in two consecutive stages, each serving a specific purpose in the adaptation process. The initial stage employs Unsupervised Domain Adaptation (UDA) techniques, seeking to obtain a shared-features representation by utilizing both labeled source and unlabeled target data sets. However, the challenge arises in this first stage, as the shared representation introduces uncertainty about capturing all relevant features from the target domain. This uncertainty may lead to model instability, especially in scenarios with a substantial gap between the source and target domains. To address this limitation and bolster the model's stability and adaptability, the second stage of WS-UDA is introduced. In this subsequent phase, the shared representation obtained from the first stage serves as the starting point for a consecutive training stage. This informed starting point is anticipated to contribute to a more robust adaptation process, ultimately improving performance, particularly in scenarios marked by significant dissimilarities between the source and target domains.The contributions in this work are summarized as follows:  
* This work proposes WS-UDA, a sequential architecture designed to enhance domain-invariant feature representation learning in a manner akin to fine-tuning. It accomplishes sentiment analysis in an unsupervised domain adaptation framework. * WS-UDA model exhibits the capability to identify deeper features beneficial to the target domain, leading to improved overall performance. * The experiments conducted on three benchmark data sets, encompassing both single-source/single-target domain and multi-source/single-target domain settings, reveal notable enhancements in the performance of unsupervised domain adaptation when employing the proposed WS-UDA model
* Experiments also demonstrate that a greater percentage of improvement can be achieved the more distant the domains. The subsequent sections are organized as follows: Section 2 offers a comprehensive review of unsupervised domain adaptation. In Section 3, we articulate the problem statement and motivation. Section 4 introduces the proposed Weighted Sequential Unsupervised Domain Adaptation (WS-UDA) framework, delving into its architectural design and key components.