A fused syntactic information tree model for aspect-level sentiment analysis

JinYu Zhao; Qiang Li; CongCong Li; BoWen He; ZhaoYun Zhang

doi:10.1117/12.2684663

21 July 2023 A fused syntactic information tree model for aspect-level sentiment analysis

JinYu Zhao, Qiang Li, CongCong Li, BoWen He, ZhaoYun Zhang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127172V (2023) https://doi.org/10.1117/12.2684663
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Aspect-level sentiment analysis aims to determine the sentiment polarity of different aspects contained in text sentences. To address the problem that current neural network models based on LSTM and attention mechanisms cannot effectively encode aspect features and sentiment features, thus leading to a less than reasonable representation of text information, this paper proposes a fused syntactic information tree SITM-Bi-LSTM model for aspect-level sentiment analysis. First, the text sequence is passed through a bidirectional LSTM neural network to obtain its hidden output representation containing contextual information. Then, the syntactic information is used in the syntactic path to focus on the influence of contextual words with different distances from the aspect on its sentiment polarity, which in turn achieves the effect of enhancing the aspectual feature representation. Finally, the Tan-Relu gating unit is constructed to selectively extract emotional features that match the given aspect information for determining the emotional polarity of the aspect. The experimental results on the Laptop and Restaurant datasets show that the accuracy and values of the SITM-Bi-LSTM model are better than those of the comparison models, which confirms the model's effectiveness.

Citation Download Citation

JinYu Zhao, Qiang Li, CongCong Li, BoWen He, and ZhaoYun Zhang "A fused syntactic information tree model for aspect-level sentiment analysis", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127172V (21 July 2023); https://doi.org/10.1117/12.2684663

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KEYWORDS

Data modeling

Neural networks

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Education and training

Feature extraction

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