Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning (2025)

Abstract: Cross-domain aspect-based sentiment analysis (ABSA) aims at learning specific knowledge from a source domain to perform various ABSA tasks on a target domain. Recent works mainly focus on how to use domain adaptation techniques to transfer the domain-agnostic features from the labeled source domain to the unlabeled target domain. However, it would be unwise to manually collect a large number of unlabeled data from the target domain, where such data may not be available owing to the facts like data security concerns in banking or insurance. To alleviate this issue, we propose ZeroABSA, a unified zero-shot learning framework for cross-domain ABSA that effectively eliminates dependency on target-domain annotations. Specifically, ZeroABSA consists of two novel components, namely, (1) A hybrid data augmentation module leverages large language models (LLMs) to synthesize high-quality, domain-adaptive target-domain data, by evaluating the generated samples across vocabulary richness, semantic coherence and sentiment/domain consistency, followed by iterative refinement; (2) A domain-aware chain-of-thought (COT) prompting strategy trains models on augmented data while explicitly modeling domain-invariant reasoning to bridge the well-known cross-domain gap. Extensive evaluations across four diverse domains demonstrate that ZeroABSA surpasses the-state-of-the-arts, which effectively advances the practicality of cross-domain ABSA in real-world scenarios where labeled target-domain data is unavailable.

## introduction aspect-based sentiment analysis (absa) is a widely-discussed fine-grained sentiment analysis task @xcite , aims at identifying sentiment targets within sentences to form the structured pairs @xmath0, polarity>, where the polarity "positive" is a specific sentiment towards a target aspect "food" in sentence "the food at this restaurant is good." this end-to-end formulation has * corresponding author evolved into three principal subtasks: (1) aspect term extraction (ate), isolating domain-spcific aspect terms from sentences @xcite ; @xcite aspect sentiment classification (asc), predicting the sentiment polarities for given terms @xcite ; and (3) aspect sentiment triplet extraction (aste), extending initial absa to a triplet (e.g., "<food, good, @xmath1, capturing richer contextual sementics @xcite @xcite . however, these paradigms still restricted to domain-specific data scarcity in low-resource domains. therefore, many researchers tackle various absa tasks beyond a specific domain. they primarily focus on cross-domain sentiment correlations by aligning latent feature distributions across domains, which is known as cross-domain absa @xcite @xcite , leveraging the availability of a tremendous amount of sentiments expressed across different domains. the principle of such methods is to employ unsupervised domain adaptation (uda) techniques to learn domain-invariant features for various crossdomain absa tasks, which, however, always heavily relies on collected unlabeled data from the target domain to minimize the domain gap @xcite @xcite . however, this approach faces a significant challenge: unlabeled target domain data is often scarce in practice, especially in domains with strict data security, like banking or insurance. recent advancements have explored the use of pre-trained language models for data augmentation in cross-domain absa tasks @xcite @xcite . for instance, yu et al. combines domain-adaptive pseudo-labeling with language modeling to improve the effectiveness of cross-domain data augmentation. however, these approaches still depend on unlabeled target domain data to generate pseudo-labeled data. furthermore, the common approach of training first on labeled source domain data and then on generated target domain data @xcite can lead to inconsistencies. the generated target domain data often differ significantly from the source domain data, causing difficulties in maintaining domain-specific awareness during inference. this can result in models struggling to bridge the gap between source and target domains effectively, ultimately impacting performance. we define zero-shot cross-domain absa as a challenging setting where the model is trained without any data from the target domain. in this work, our zero-shot setting aims to overcome the critical limitation of data scarcity and privacy concerns in low-resource domains by completely eliminating the need for target domain data. to overcome these limitations, we propose a novel zero-shot crossdomain absa framework that achieves crossdomain invariant feature learning and annotationfree knowledge transfer, comprising: 1) zero-shot data augmentation: we utilize large language models (llms) to synthesize target domain data via target domain weak supervision (e.g., domain names) with the existing labeled source domain data. by harnessing in-context learning, we generate diverse, semantically coherent simulated target domain examples. a subset is further augmented with reference-free generation to maximize diversity, facilitating learning of domain-invariant representations that generalize to target domain characteristics. 2) evaluation of generated data: to ensure the quality and fluency of the generated data, we first calculate its vocabulary richness using shannon entropy. additionally, we evaluate the data by calculating domain consistency, sentiment consistency, and sentence fluency using a ranking model. based on these metrics, we select the highest-quality data and combine it with existing domain data for model training. ## methodology 3.1 problem definition and notations based on the previous work on defining the absa task, given a sentence @xmath2, w 2 , . . . , w n } with n words, the goal of the absa task is to extract several tuples @xmath3, p i )} ## conclusion in this paper, we introduced zeroabsa, a novel zero-shot framework for cross-domain absa that completely eliminates the need for target domain data. our framework effectively bridges the domain gap by integrating a large language model-driven augmentation module with a domain-contextualized chain-of-thought (dccot) strategy. the llm module synthesizes high-quality, domain-adaptive data, while dccot guides the model to learn domain-invariant features. our comprehensive experiments demonstrated that ze-roabsa significantly outperforms existing methods, validating its effectiveness and robustness in zero-shot settings.