Dependency Parsing and Attention Network for Aspect-Level Sentiment Classification

Abstract

Aspect-level sentiment classification aims to determine the sentiment polarity of the sentence towards the aspect. The key element of this task is to characterize the relationship between the aspect and the contexts. Some recent attention-based neural network methods regard the aspect as the attention calculation goal, so they can learn the association between aspect and contexts directly. However, the above attention model simply uses the word embedding to represent the aspect, it fails to make a further improvement on the performance of aspect sentiment classification. To solve this problem, this paper proposes a dependency subtree attention network (DSAN) model. The DSAN model firstly extracts the dependency subtree that contains the descriptive information of the aspect based on the dependency tree of the sentence, and then utilizes a bidirectional GRU network to generate an accurate aspect representation, and uses the dot-product attention function for the dependency subtree aspect representation, which finally yields the appropriate attention weights. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the DSAN model.