A Fused Syntactic Information Tree Model For Aspect-level Sentiment Analysis

doi:10.1117/12.2684663

	A Fused Syntactic Information Tree Model For Aspect-level Sentiment Analysis
	Zhao, JinYu1 ; Li, Qiang 1,2; Li, CongCong1 ; He, BoWen1 ; Zhang, ZhaoYun1
	2023
会议名称	3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023
会议录名称	Proceedings of SPIE - The International Society for Optical Engineering
卷号	12717
会议日期	March 31, 2023 - April 2, 2023
会议地点	Wuhan, China
会议录编者/会议主办者	Academic Exchange Information Centre (AEIC) ; National and Kapodistrian University of Athens
出版者	SPIE
摘要	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. Finally, 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. © 2023 SPIE.
关键词	Long short-term memory Syntactics 'current Affective feature Aspectual entity Attention mechanisms Gating unit Model-based OPC Neural network model Sentiment analysis Syntactic information Tree modeling
DOI	10.1117/12.2684663
收录类别	EI
语种	英语
EI入藏号	20233814741345
EI主题词	Sentiment analysis
EI分类号	723.2 Data Processing and Image Processing
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/35353
专题	信息工程与人工智能学院
通讯作者	Li, Qiang
作者单位	1.School of Information Engineering, Lanzhou University of Finance and Economics, Gansu, Lanzhou, China; 2.Key Laboratory of Electronic Commerce, Lanzhou University of Finance and Economics, Gansu, Lanzhou, China
通讯作者单位	兰州财经大学
推荐引用方式 GB/T 7714	Zhao, JinYu,Li, Qiang,Li, CongCong,et al. A Fused Syntactic Information Tree Model For Aspect-level Sentiment Analysis[C]//Academic Exchange Information Centre (AEIC), National and Kapodistrian University of Athens:SPIE,2023.