Aspect-level sentiment analysis based on graph convolutional networks and interactive aggregate attention

Abstract

Sentiment analysis has always been an important task in the artificial intelligence field, and aspect-level sentiment analysis involves fine-grained sentiment analysis. Recently, graph convolutional networks (GCNs) built on sentence dependency trees have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, they can efficiently aggregate the information of neighboring nodes. However, many previous studies concerning graph neural networks only focused on the information between nodes and did not effectively explore the connections between aspects and sentences or highlight the parts with high aspect relevance. To address this problem, we propose a new GCN. When constructing a dependency tree-based graph, affective information and position index information are added to each node to enhance the graph. In addition, we use an interactive aggregate attention mechanism, which utilizes the aggregated information related to the connections between aspects and sentences from the GCN to highlight the important parts so that the model can fully learn the relationships between aspects and sentences. Finally, we validate our model on four public benchmark datasets and attain improvements over the state-of-the-art methods.