Article citationsMore>>

Wang, Y.Q., Huang, M.L., Zhu, X.Y., et al. (2016) Attention-Based LSTM for Aspect-Level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, November 2016, 606-615.
https://doi.org/10.18653/v1/D16-1058

has been cited by the following article:

• 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.