Interactive Relation Graph Attention Network Model for Aspect-Based Sentiment Analysis

Aspect-level sentiment analysis aims to analysis the sentiment polarity of each aspect of online review, and it is a fine-grained sentiment analysis technique. There have been many related studies that have combined syntactic dependency trees with graph attention networks and have applied them to this task with good results. To address the problems that previous studies have ignored information about relation types, have not fully explored the potential semantic information contained in relation types, and have ignored the connection between dependency relations and relation types, an interactive relation graph attention network（IRGAT）model based on graph attention networks is proposed. The model extracts feature information of relational types and then makes them learn interactively with the contextual feature information extracted by the graph attention network, so that they are connected to each other and strengthen their respective feature representations. Finally, the features are fused through the aspect attention mechanism, and then a classifier is used to capture the sentiment classification results. The model is tested on four publicly datasets. The experimental results show that the IRGAT model improves the percent of prediction accuracy and MF1 values by an average of 1.52 and 1.56 percentage points respectively compared to existing aspect-level sentiment analysis models. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.