Bidirectional Directed Acyclic Graph Neural Network for Aspect-level Sentiment Classification

To achieve outstanding aspect-level sentiment analysis (ASC), it is crucial to reduce the distance between aspect terms and opinion words. Recently, advanced methods in ASC used graph neural network (GNN)based methods to leverage the syntactic dependency within the sentence, which can shorten the distance through syntactical dependencies. However, existing approaches that utilize GNNs have difficulty extracting long-distance relations in the dependency tree due to the over-smoothing problem resulting from stacking GNN layers, which limits their ability to detect remote relations. To solve this issue, we propose a Bidirectional Directed Acyclic Graph (BDAG) to reconstruct syntactic dependencies and a Bidirectional Directed Acyclic Graph Neural Network (BDAGNN) to efficiently propagate multi-hop sentiment information. We also enhance the BDAG with affective commonsense knowledge from SenticNet for comprehensive sentiment classification. The BDAGNN we proposed obtains partial state-of-the-art performance on four benchmark datasets, indicating the feasibility of encoding syntactic structures with BDAG.