Jiao Jiajia, Wang Haijie, Shen Ruirui, Lu Zhuo
College of Information Engineering, Shanghai Maritime University, Shanghai, China.
Math Biosci Eng. 2024 Feb 5;21(3):3498-3518. doi: 10.3934/mbe.2024154.
Aspect-level sentiment analysis can provide a fine-grain sentiment classification for inferring the sentiment polarity of specific aspects. Graph convolutional network (GCN) becomes increasingly popular because its graph structure can characterize the words' correlation for extracting more sentiment information. However, the word distance is often ignored and cause the cross-misclassification of different aspects. To address the problem, we propose a novel dual GCN structure to take advantage of word distance, syntactic information, and sentiment knowledge in a joint way. The word distance is not only used to enhance the syntactic dependency tree, but also to construct a new graph with semantic knowledge. Then, the two kinds of word distance assisted graphs are fed into two GCNs for further classification. The comprehensive results on two self-collected Chinese datasets (MOOC comments and Douban book reviews) as well as five open-source English datasets, demonstrate that our proposed approach achieves higher classification accuracy than the state-of-the-art methods with up to 1.81x training acceleration.
方面级情感分析可以提供细粒度的情感分类，以推断特定方面的情感极性。图卷积网络（GCN）越来越受欢迎，因为其图结构可以表征单词之间的相关性，从而提取更多的情感信息。然而，单词距离常常被忽略，导致不同方面的交叉错误分类。为了解决这个问题，我们提出了一种新颖的双GCN结构，以联合利用单词距离、句法信息和情感知识。单词距离不仅用于增强句法依存树，还用于构建具有语义知识的新图。然后，将这两种单词距离辅助图输入到两个GCN中进行进一步分类。在两个自行收集的中文数据集（慕课评论和豆瓣书评）以及五个开源英文数据集上的综合结果表明，我们提出的方法比现有方法具有更高的分类准确率，训练加速高达1.81倍。
