Text sentiment analysis is a fundamental task in the field of natural language processing (NLP). Recently, graph neural networks (GNNs) have achieved excellent performance in various NLP tasks. However, a GNN only considers the adjacent words when updating the node representations of the graph, and thus the model can only focus on the local features while ignoring global features. In this paper, we propose a novel multi-level graph neural network (MLGNN) for text sentiment analysis. To consider both local features and global features, we apply node connection windows with different sizes at different levels. Particularly, we integrate a scaled dot-product attention mechanism as a message passing mechanism into our method for fusing the features of each word node in the graph. The experimental results demonstrated that the proposed model outperformed other models in text sentiment analysis tasks.

文本情感分析是自然语言处理（NLP）领域的一项基本任务。最近，图神经网络（GNN）在各种NLP任务中都取得了出色的性能。但是，GNN在更新图的节点表示时仅考虑相邻单词，因此模型只能关注局部特征，而忽略全局特征。在本文中，我们提出了一种用于文本情感分析的新型多级图神经网络（MLGNN）。为了同时考虑局部特征和全局特征，我们在不同级别应用大小不同的节点连接窗口。特别是，我们将缩放的点积注意机制作为消息传递机制集成到我们的方法中，以融合图中每个单词节点的特征。