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Aspect-Specific Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Classification
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DOI：10.1109/ACCESS.2020.3012637.png)
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Abstract
En 中文
Aspect-based sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. There are two main problems with existing methods: First, the methods simply take the average of the sentence and aspect word vectors as the sentence and aspect representations for a certain sentence, but they are not explicit representations and will lose considerable useful information. Second, existing models based on graph convolutional networks (GCNs) only use the dependency relationship of a sentence, which cannot fully exploit the potential of the sentence and exert the powerful feature fusion ability of GCNs. To solve these problems, we propose a novel GCN-based model that uses a heterogeneous graph. We explicitly define sentence and aspect nodes to learn the sentence and aspect representations separately and then combine 4 kinds of relationships to construct the heterogeneous graph. In our experiments conducted on 5 public datasets, the experimental results show that our network consistently outperforms the state-of-the-art model on all these datasets.
Keywords:
Task analysis
Feature extraction
Syntactics
Context modeling
Sentiment analysis
Biological neural networks
Aspect-based sentiment classification (ABSA)
heterogeneous graph
graph convolutional network (GCN)
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