Modeling multiple latent information graph structures via graph convolutional network for aspect-based sentiment analysis

Abstract

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of aspects in a sentence. Recently, graph convolution network (GCN) model combined with attention mechanism has been used for ABSA task over graph structures, achieving promising results. However, these methods of modeling over graph structure fail to consider multiple latent information in the text, i.e., syntax, semantics, context, and so on. In addition, the attention mechanism is vulnerable to noise in sentences. To tackle these problems, in this paper, we construct an efficient text graph and propose a matrix fusion-based graph convolution network (MFLGCN) for ABSA. First, the graph structure is constructed by combining statistics, semantics, and part of speech. Then, we use the sequence model combined with the multi-head self-attention mechanism to obtain the feature representation of the context. Subsequently, the text graph structure and the feature representation of context are fed into GCN to aggregate the information around aspect nodes. The attention matrix is obtained by combining sequence model, GCN and the attention mechanism. Besides, we design a filter layer to alleviate the noise problem in the sentence introduced by the attention mechanism. Finally, in order to make the context representation more effective, attention and filtering matrices are integrated into the model. Experimental results on four public datasets show that our model is more effective than the previous models, demonstrating that using our text graph and matrix fusion can significantly empower ABSA models.

A Joint Learning Model for Aspect-based Sentiment Analysis via Machine Reading Comprehension and Graph Convolutional Network

“…Compared to the sequence models used by previous researchers [17][18][19] to solve ABSA construction, the graph convolutional neural network (GCN) models 20 are better at handling relatively complex text structures and aggregating more latent text information. In sentiment classification (SC) tasks, many researchers [21][22][23][24] have shown better performance in models built based on GCN. The basic idea is to transform the text into a graph structure based on latent semantic information, and then use the GCN to propagate information from syntax neighborhood opinion words to aspect words.…”