  Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment
polarity of a sentence toward a specific aspect. Recently, it has been shown
that dependency trees can be integrated into deep learning models to produce
the state-of-the-art performance for ABSA. However, these models tend to
compute the hidden/representation vectors without considering the aspect terms
and fail to benefit from the overall contextual importance scores of the words
that can be obtained from the dependency tree for ABSA. In this work, we
propose a novel graph-based deep learning model to overcome these two issues of
the prior work on ABSA. In our model, gate vectors are generated from the
representation vectors of the aspect terms to customize the hidden vectors of
the graph-based models toward the aspect terms. In addition, we propose a
mechanism to obtain the importance scores for each word in the sentences based
on the dependency trees that are then injected into the model to improve the
representation vectors for ABSA. The proposed model achieves the
state-of-the-art performance on three benchmark datasets.
