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Aspect level sentiment classification aims to identify the sentimentexpressed towards an aspect given a context sentence. Previous neural networkbased methods largely ignore the syntax structure in one sentence. In thispaper, we propose a novel target-dependent graph attention network (TD-GAT) foraspect level sentiment classification, which explicitly utilizes the dependencyrelationship among words. Using the dependency graph, it propagates sentimentfeatures directly from the syntactic context of an aspect target. In ourexperiments, we show our method outperforms multiple baselines with GloVeembeddings. We also demonstrate that using BERT representations furthersubstantially boosts the performance.

Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks