  Aspect-level sentiment analysis aims to determine the sentiment polarity
towards a specific target in a sentence. The main challenge of this task is to
effectively model the relation between targets and sentiments so as to filter
out noisy opinion words from irrelevant targets. Most recent efforts capture
relations through target-sentiment pairs or opinion spans from a word-level or
phrase-level perspective. Based on the observation that targets and sentiments
essentially establish relations following the grammatical hierarchy of
phrase-clause-sentence structure, it is hopeful to exploit comprehensive
syntactic information for better guiding the learning process. Therefore, we
introduce the concept of Scope, which outlines a structural text region related
to a specific target. To jointly learn structural Scope and predict the
sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to
synthesize information from constituency tree and dependency tree, exploring
the potential of linking two syntax parsing methods to enrich the
representation. Experimental results on four public datasets illustrate that
our HGCN model outperforms current state-of-the-art baselines.
