Aspect-category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect-category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect-category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra-graph convolution and inter-graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models.

中文翻译：

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基于混合节点的张量图卷积网络用于微博评论的aspect-category情感分类

微博评论方面-类别情感分类旨在识别微博评论中不同观点方面的情感极性，对舆情分析具有重要意义。目前，大多数aspect-category情感分类方法需要大量的标注数据，将评论视为独立样本，没有利用评论之间的关系。本文提出了一种基于张量图卷积网络的aspect-category情感分类方法。首先，一条评论与其aspect category的组合被认为是一个混合节点，混合节点的原始表示由Bert模型编码。其次，根据混合节点之间的语义相似性和情感相关性构建情感图和语义图，它们被堆叠成一个张量。然后对每一层图张量进行图内卷积和图间卷积两个卷积操作。这样，混合节点就可以学习和合并不同图的异构信息。最后，在少量标记评论的监督下，可以根据混合节点的特征完成情感分类。在两个微博数据集上的实验结果表明，与其他基线模型相比，所提出的模型可以显着提高情感分类的性能。最后，在少量标记评论的监督下，可以根据混合节点的特征完成情感分类。在两个微博数据集上的实验结果表明，与其他基线模型相比，所提出的模型可以显着提高情感分类的性能。最后，在少量标记评论的监督下，可以根据混合节点的特征完成情感分类。在两个微博数据集上的实验结果表明，与其他基线模型相比，所提出的模型可以显着提高情感分类的性能。