Graph convolutional network with target-attention for aspect-based sentiment analysis
Xiajiong Shen, Huijing Yang, Yiru Han, Xianjin Shi · 2023
Aspect Sentiment Classification (ASC) is aimed at determining the sentiment disposition towards a specific target. Most previous approaches use RNN and attention mechanisms to model context and target words. However, these approaches ignore the syntactic relationship between the aspect terms and the corresponding context words, resulting in the models wrongly focusing on syntax-independent words. To solve this problem, we propose an attention fusion model based on a Graph Convolutional Network (GCN) that considers syntactic and semantic information of sentences as well as captures more comprehensive information using a Feature Interactive Learning mechanism. We also apply the Pre-training model Bert to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the efficacy of the model.