  I propose a novel dual-attention model(DAM) for aspect-level sentiment
classification. Many methods have been proposed, such as support vector
machines for artificial design features, long short-term memory networks based
on attention mechanisms, and graph neural networks based on dependency parsing.
While these methods all have decent performance, I think they all miss one
important piece of syntactic information: dependency labels. Based on this
idea, this paper proposes a model using dependency labels for the attention
mechanism to do this task. We evaluate the proposed approach on three datasets:
laptop and restaurant are from SemEval 2014, and the last one is a twitter
dataset. Experimental results show that the dual attention model has good
performance on all three datasets.
