基于深度BiLSTM和图卷积网络的方面级情感分析

现有方面级情感分析方法，存在无法获取最优文本表示和使用普通图卷积网络不能提取依存图中深层结构信息的问题。为此，提出了一种基于深度BiLSTM（DBiLSTM）和紧密连接的图卷积网络（DDGCN）模型。首先,通过DBiLSTM获取方面词与上下文单词间的深层语义信息；其次，在原始图卷积网络中加入紧密连接，以生成能提取深层结构信息的紧密图卷积网络；然后，利用改进后的图卷积网络捕获依存图上的结构信息；最终，将融合2种深层信息的文本表示用于情感分类。3个数据集上的实验结果表明，DDGCN模型相比对比模型在准确度和F1上均有提升。

Aspect-level sentiment analysis based ondeep BiLSTM and graph convolutional networks

In the existing aspect-level sentiment analysis methods, there are problems that the optimal text representation cannot be obtained, and the general graph convolutional network cannot be used to extract the deep structural information in the dependency graph. For these problems, this paper proposes a graph convolutional network (DDGCN) model based on deep BiLSTM and dense connections. Firstly, the deep semantic information between aspect words and context words is obtained by deep BiLSTM. Secondly, dense connections are added to the original graph convolutional network to generate a dense graph convolutional network that can extract the deep structural information. Then, the improved graph convolutional network is used to capture the structural information on the dependency graph. Finally, the text representation combining the two deep information is used for sentiment classification. Experimental results on three datasets show that DDGCN model improves both accuracy and F1 compared with the comparative model.