  Targeted sentiment classification predicts the sentiment polarity on given
target mentions in input texts. Dominant methods employ neural networks for
encoding the input sentence and extracting relations between target mentions
and their contexts. Recently, graph neural network has been investigated for
integrating dependency syntax for the task, achieving the state-of-the-art
results. However, existing methods do not consider dependency label
information, which can be intuitively useful. To solve the problem, we
investigate a novel relational graph attention network that integrates typed
syntactic dependency information. Results on standard benchmarks show that our
method can effectively leverage label information for improving targeted
sentiment classification performances. Our final model significantly
outperforms state-of-the-art syntax-based approaches.
