  Aspect-level sentiment classification aims to distinguish the sentiment
polarities over one or more aspect terms in a sentence. Existing approaches
mostly model different aspects in one sentence independently, which ignore the
sentiment dependencies between different aspects. However, we find such
dependency information between different aspects can bring additional valuable
information. In this paper, we propose a novel aspect-level sentiment
classification model based on graph convolutional networks (GCN) which can
effectively capture the sentiment dependencies between multi-aspects in one
sentence. Our model firstly introduces bidirectional attention mechanism with
position encoding to model aspect-specific representations between each aspect
and its context words, then employs GCN over the attention mechanism to capture
the sentiment dependencies between different aspects in one sentence. We
evaluate the proposed approach on the SemEval 2014 datasets. Experiments show
that our model outperforms the state-of-the-art methods. We also conduct
experiments to evaluate the effectiveness of GCN module, which indicates that
the dependencies between different aspects is highly helpful in aspect-level
sentiment classification.
