华中师范大学学报（自然科学版）2026，Vol.60Issue(2)：284-295,12.DOI:10.19603/j.cnki.1000-1190.2026.02.011
融合多子空间及通道注意力的多模态情感分析
Multimodal sentiment analysis fusing multi-subspace and channel attention
摘要
Abstract
Current multimodal sentiment analysis primarily relies on complex techniques for fusing multimodal features.However,due to the significant distribution differences among various modal features,direct fusion yields poor results.To address this issue,this paper proposes an interactive learning network model that integrates a multi-subspace framework and channel attention.Firstly,a hybrid neural network is utilized to extract features from each modality,and a stacked bidirectional long short-term memory network is employed to represent the utterance sequence at the linguistic level.Fixed-size utterance vectors are mapped into two different representations:modal-invariant and modal-specific,with the latter undergoing bimodal interaction using a temporal convolutional network.Subsequently,channel attention is leveraged to extract more meaningful information,and a cross-modal interactive bidirectional gated recurrent neural network and a bimodal interactive attention mechanism are proposed for deeper interaction among the extracted modal-invariant representation vectors.Loss optimization is then performed using a loss function.Finally,a multi-head attention mechanism based on Transformer is executed to obtain a joint vector,and a fully connected layer is utilized to predict the final result.Experiments conducted on the CMU-MOSI and CMU-MOSEI datasets demonstrate that this method can effectively eliminate multimodal differences and achieve multimodal fusion.关键词
多模态情感分析/混合神经网络/多模态融合/Transformer/注意力机制Key words
multimodal sentiment analysis/hybrid neural network/multimodal fusion/Transformer/attention mechanism分类
信息技术与安全科学引用本文复制引用
米小锋,王旭阳,史浩君..融合多子空间及通道注意力的多模态情感分析[J].华中师范大学学报（自然科学版）,2026,60(2):284-295,12.基金项目
国家自然科学基金项目(62161019). （62161019）
