  Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently.
