Large language model augmented syntax-aware domain adaptation method for aspect-based sentiment analysis
Haochen Zou, Yongli Wang
Abstract
Cross-domain aspect-based sentiment analysis aims to leverage knowledge from the source domain to identify the sentiment polarity towards a given aspect attribute in the text content from the target domain. Existing domain adaptation approaches either focus on acquiring domain-independent shared feature representations or adjusting the obtained feature distribution to the target domain, which fails to address critical domain-specific attributes, leading to misaligned feature representations. We propose a large language model augmented syntax-aware domain adaptation method that integrates advanced large language models with structured syntactic knowledge to recognize semantic attributes and address the lack of syntax sensitivity in large language models. A domain topic predictor based on adversarial training is developed to enhance the robustness and generalization of the framework across different domains. Additionally, automatic soft prompt learning is conducted based on analysed domain topics and task-relevant feature representations for domain-specific fine-tuning, aiding the architecture in conveying domain-specific semantic information in the cross-domain environment. The feature aggregation approach dynamically fuses six categories of analysed feature representations for fine-grained sentiment classification. To the best of our knowledge, this study represents the pioneering effort to systematically leverage syntactic and cross-domain characteristics to enhance pre-trained large language models in addressing cross-domain aspect-based sentiment analysis tasks. Experimental results on publicly available benchmark datasets validate the effectiveness of the proposed architecture.