  Aspect level sentiment classification aims to identify the sentiment
expressed towards an aspect given a context sentence. Previous neural network
based methods largely ignore the syntax structure in one sentence. In this
paper, we propose a novel target-dependent graph attention network (TD-GAT) for
aspect level sentiment classification, which explicitly utilizes the dependency
relationship among words. Using the dependency graph, it propagates sentiment
features directly from the syntactic context of an aspect target. In our
experiments, we show our method outperforms multiple baselines with GloVe
embeddings. We also demonstrate that using BERT representations further
substantially boosts the performance.
