Joint Learning of Local and Global Features for Aspect-based Sentiment
  Classification
Hao Niu Yun Xiong Xiaosu Wang Philip S. Yu
Aspect-based sentiment classification (ASC) aims to judge the sentiment
polarity conveyed by the given aspect term in a sentence. The sentiment
polarity is not only determined by the local context but also related to the
words far away from the given aspect term. Most recent efforts related to the
attention-based models can not sufficiently distinguish which words they should
pay more attention to in some cases. Meanwhile, graph-based models are coming
into ASC to encode syntactic dependency tree information. But these models do
not fully leverage syntactic dependency trees as they neglect to incorporate
dependency relation tag information into representation learning effectively.
In this paper, we address these problems by effectively modeling the local and
global features. Firstly, we design a local encoder containing: a Gaussian mask
layer and a covariance self-attention layer. The Gaussian mask layer tends to
adjust the receptive field around aspect terms adaptively to deemphasize the
effects of unrelated words and pay more attention to local information. The
covariance self-attention layer can distinguish the attention weights of
different words more obviously. Furthermore, we propose a dual-level graph
attention network as a global encoder by fully employing dependency tag
information to capture long-distance information effectively. Our model
achieves state-of-the-art performance on both SemEval 2014 and Twitter
datasets.