  Multi-domain aspect-based sentiment analysis (ABSA) seeks to capture
fine-grained sentiment across diverse domains. While existing research narrowly
focuses on single-domain applications constrained by methodological limitations
and data scarcity, the reality is that sentiment naturally traverses multiple
domains. Although large language models (LLMs) offer a promising solution for
ABSA, it is difficult to integrate effectively with established techniques,
including graph-based models and linguistics, because modifying their internal
architecture is not easy. To alleviate this problem, we propose a novel
framework, Feature-aware In-context Learning for Multi-domain ABSA (FaiMA). The
core insight of FaiMA is to utilize in-context learning (ICL) as a
feature-aware mechanism that facilitates adaptive learning in multi-domain ABSA
tasks. Specifically, we employ a multi-head graph attention network as a text
encoder optimized by heuristic rules for linguistic, domain, and sentiment
features. Through contrastive learning, we optimize sentence representations by
focusing on these diverse features. Additionally, we construct an efficient
indexing mechanism, allowing FaiMA to stably retrieve highly relevant examples
across multiple dimensions for any given input. To evaluate the efficacy of
FaiMA, we build the first multi-domain ABSA benchmark dataset. Extensive
experimental results demonstrate that FaiMA achieves significant performance
improvements in multiple domains compared to baselines, increasing F1 by 2.07%
on average. Source code and data sets are anonymously available at
https://github.com/SupritYoung/FaiMA.
