Sentiment analysis has always been an important basic task in the NLP field. Recently, graph convolutional networks (GCNs) have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, every node can contain neighboring node information. However, in previous studies, most models used only a single GCN to learn contextual information. The GCN relies on the construction method of the graph, and a single GCN will cause the model to focus on a certain relationship of nodes that depends on the construction method and ignore other information. In addition, when the GCN aggregates node information, it cannot determine whether the aggregated information is useful, so it will inevitably introduce noise. We propose a model that fuses two parallel GCNs to learn different relational features between sentences at the same time, and we add a gate mechanism to the GCN to filter the noise introduced by the GCN when aggregating information. Finally, we validate our model on public datasets, and the experiments show that compared to state-of-the-art models, our model performs better.

情感分析一直是NLP领域的重要基础工作。最近，图卷积网络（GCN）已广泛用于方面级情感分析。由于 GCN 具有良好的聚合效果，每个节点都可以包含相邻节点信息。然而，在之前的研究中，大多数模型仅使用单个 GCN 来学习上下文信息。GCN依赖于图的构造方法，单一的GCN会导致模型关注依赖于构造方法的节点的某种关系而忽略其他信息。此外，GCN在聚合节点信息时，无法判断聚合后的信息是否有用，因此不可避免地会引入噪声。我们提出了一种融合两个并行 GCN 的模型，以同时学习句子之间的不同关系特征，并且我们在 GCN 中添加了一个门机制，以过滤 GCN 在聚合信息时引入的噪声。最后，我们在公共数据集上验证了我们的模型，实验表明，与最先进的模型相比，我们的模型表现更好。