Abstract
Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN.
Highlights
Aspect-based sentiment classification (ABSC) is a key subtask of sentiment classificaAspect-based sentiment classification (ABSC)is a of key subtask of sentiment classifi-Its objective tion [1], which is a fine-grained task in the field text sentiment classifiction.cation [1], which is a fine-grained task in the field of text sentiment classifiction
This paper proposed an attention-based graph convolutional network model
We propose to adopt the syntactic dependency structure in sentences and solve the problem of long-distance multi-word dependence based on aspect-based sentiment classification; We design a bidirectional attention mechanism to enhance the interaction between aspect and context to obtain aspect specific context representation; Experiments on three benchmarking collections indicated that the effectiveness of our model compared with other popular models
Summary
Aspect-based sentiment classification (ABSC) is a key subtask of sentiment classificaAspect-based sentiment classification (ABSC). Sci. 2021, 11, 1528 this model used two LSTM networks to generate aspect-based sentiment classification results, but it cannot obtain important clues of the aspect. The attention mechanism can well solve the problem of determining the sentiment polarity of each aspect in the sentence It was originally used for image processing to enable neural networks to focus on certain information when processing data. We propose to adopt the syntactic dependency structure in sentences and solve the problem of long-distance multi-word dependence based on aspect-based sentiment classification; We design a bidirectional attention mechanism to enhance the interaction between aspect and context to obtain aspect specific context representation; Experiments on three benchmarking collections indicated that the effectiveness of our model compared with other popular models
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