  In this work we investigate the capability of Graph Attention Network for
extracting aspect and opinion terms. Aspect and opinion term extraction is
posed as a token-level classification task akin to named entity recognition. We
use the dependency tree of the input query as additional feature in a Graph
Attention Network along with the token and part-of-speech features. We show
that the dependency structure is a powerful feature that in the presence of a
CRF layer substantially improves the performance and generates the best result
on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment
with additional layers like BiLSTM and Transformer in addition to the CRF
layer. We also show that our approach works well in the presence of multiple
aspects or sentiments in the same query and it is not necessary to modify the
dependency tree based on a single aspect as was the original application for
sentiment classification.
