  Aspect-based sentiment analysis aims to determine the sentiment polarity
towards a specific aspect in online reviews. Most recent efforts adopt
attention-based neural network models to implicitly connect aspects with
opinion words. However, due to the complexity of language and the existence of
multiple aspects in a single sentence, these models often confuse the
connections. In this paper, we address this problem by means of effective
encoding of syntax information. Firstly, we define a unified aspect-oriented
dependency tree structure rooted at a target aspect by reshaping and pruning an
ordinary dependency parse tree. Then, we propose a relational graph attention
network (R-GAT) to encode the new tree structure for sentiment prediction.
Extensive experiments are conducted on the SemEval 2014 and Twitter datasets,
and the experimental results confirm that the connections between aspects and
opinion words can be better established with our approach, and the performance
of the graph attention network (GAT) is significantly improved as a
consequence.
