TITLE:
Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information
AUTHORS:
Jiachen Yang, Yegang Li, Hao Zhang, Junpeng Hu, Rujiang Bai
KEYWORDS:
Aspect-Level Sentiment Analysis, Attentional Mechanisms, Dependent Syntactic Trees, Graph Convolutional Neural Networks
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.1,
January
31,
2024
ABSTRACT: Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.