Semisupervised Hierarchical Subspace Learning Model for Multimodal Social Media Sentiment Analysis

doi:10.1109/TCE.2024.3350696

CSpace

	Semisupervised Hierarchical Subspace Learning Model for Multimodal Social Media Sentiment Analysis
	Han, Xue 1; Cheng, Honlin 2; Ding, Jike 1; Yan, Suqin 1
	2024-02
发表期刊	IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
ISSN	0098-3063
卷号	70 期号:1 页码:3446-3454
摘要	The multimodal data analysis model combined with text and image has gradually become an important approach for sentiment analysis in social media. This study proposes a semisupervised hierarchical subspace learning (SHSL) model to address the issue of insufficient labeled samples in multimodal sentiment analysis. The SHSL model captures potential feature representations of multimodal data in a low-rank subspace, at the same time, it adaptively assigns a weight to each modality. As a result, multimodal data can share the potential representation in the low-rank subspace. The SHSL model continuously projects the shared potential representation into the semantic space and achieves label propagation, to link shared potential representations with emotional states in the semantic space. The low-rank subspace serves as a bridge between the original space and the semantic space. It not only enriches the structure of feature space, but also reconstructs original high-dimensional data from low-dimensional features. In addition, the SHSL model constrains the class labels of unlabeled data to satisfy the non-negativity and normalization properties of rows to improve the model performance. Comparative experiments are conducted on the MVSA-single and MVSA-multiple datasets, and the experimental results demonstrate that the proposed model has excellent sentiment analysis capabilities.
关键词	Feature extraction Sentiment analysis Data models Semantics Analytical models Dictionaries Data mining Multimodal data sentiment analysis semisupervised learning subspace learning
DOI	10.1109/TCE.2024.3350696
收录类别	SCIE
语种	英语
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001244845500041
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
原始文献类型	Article
EISSN	1558-4127
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.library.ouchn.edu.cn/handle/39V7QQFX/171077
专题	国家开放大学
通讯作者	Han, Xue; Cheng, Honlin
作者单位	1.Xuzhou Open Univ, Coll Informat Engn, Xuzhou 221000, Jiangsu, Peoples R China; 2.Xuzhou Univ Technol, Sch Informat Engn, Xuzhou 221018, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Han, Xue,Cheng, Honlin,Ding, Jike,et al. Semisupervised Hierarchical Subspace Learning Model for Multimodal Social Media Sentiment Analysis[J]. IEEE TRANSACTIONS ON CONSUMER ELECTRONICS,2024,70(1):3446-3454.
APA	Han, Xue,Cheng, Honlin,Ding, Jike,&Yan, Suqin.(2024).Semisupervised Hierarchical Subspace Learning Model for Multimodal Social Media Sentiment Analysis.IEEE TRANSACTIONS ON CONSUMER ELECTRONICS,70(1),3446-3454.
MLA	Han, Xue,et al."Semisupervised Hierarchical Subspace Learning Model for Multimodal Social Media Sentiment Analysis".IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 70.1(2024):3446-3454.