  Due to their inherent capability in semantic alignment of aspects and their
context words, attention mechanism and Convolutional Neural Networks (CNNs) are
widely applied for aspect-based sentiment classification. However, these models
lack a mechanism to account for relevant syntactical constraints and long-range
word dependencies, and hence may mistakenly recognize syntactically irrelevant
contextual words as clues for judging aspect sentiment. To tackle this problem,
we propose to build a Graph Convolutional Network (GCN) over the dependency
tree of a sentence to exploit syntactical information and word dependencies.
Based on it, a novel aspect-specific sentiment classification framework is
raised. Experiments on three benchmarking collections illustrate that our
proposed model has comparable effectiveness to a range of state-of-the-art
models, and further demonstrate that both syntactical information and
long-range word dependencies are properly captured by the graph convolution
structure.
