Author(s)

Jiachen Yang¹, Yegang Li¹, Hao Zhang¹, Junpeng Hu², Rujiang Bai²

¹School of Computer Science and Technology, Shandong University of Technology, Zibo, China.
²Institute of Information Management, Shandong University of Technology, Zibo, China.

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.

Aspect-Level Sentiment Analysis, Attentional Mechanisms, Dependent Syntactic Trees, Graph Convolutional Neural Networks

Yang, J. , Li, Y. , Zhang, H. , Hu, J. and Bai, R. (2024) Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information. Journal of Computer and Communications, 12, 191-207. doi: 10.4236/jcc.2024.121014.