  Aspect-level sentiment classification aims to identify the sentiment polarity
towards a specific aspect term in a sentence. Most current approaches mainly
consider the semantic information by utilizing attention mechanisms to capture
the interactions between the context and the aspect term. In this paper, we
propose to employ graph convolutional networks (GCNs) on the dependency tree to
learn syntax-aware representations of aspect terms. GCNs often show the best
performance with two layers, and deeper GCNs do not bring additional gain due
to over-smoothing problem. However, in some cases, important context words
cannot be reached within two hops on the dependency tree. Therefore we design a
selective attention based GCN block (SA-GCN) to find the most important context
words, and directly aggregate these information into the aspect-term
representation. We conduct experiments on the SemEval 2014 Task 4 datasets. Our
experimental results show that our model outperforms the current
state-of-the-art.
