基于全局局部交互模型的多模态情感分析OA

Multimodal aspect-based sentiment analysis(MABSA)is a critical research direction in the field of affective computing,aiming to integrate multimodal information,such as text,images,and audio to achieve fine-grained analysis of sentiment toward spe-cific aspects.Current research in MABSA faces challenges such as image noise interference and excessive reliance on local features,which compromise the accuracy and comprehensiveness of the analysis.To address these limitations,this paper proposes an innova-tive global-local interactive emotion analysis model(GLIEAM).On the one hand,the model employs a tandem architecture of vi-sion transformer(Vision Transformer,ViT)and generative pre-trained transformer(GPT)to generate image descriptions,which are then concatenated with original text features,significantly enhancing information fusion and enabling a more comprehensive capture of emotional cues in multimodal data.On the other hand,to mitigate image noise,a hybrid approach combining wavelet transform and non-local means is applied for image denoising.Additionally,convolutional neural networks(CNN)and tokens-to-token vision transformer(T2T-ViT)are utilized to extract local and global image features,respectively,avoiding over-reliance on local features and achieving balanced and holistic image feature extraction.Experimental results on benchmark datasets demonstrate that the pro-posed method outperforms existing approaches,the accuracy reached 78.46%on the Twitter-15 dataset and 75.21%on the Twitter-17 dataset,particularly exhibiting superior performance in low-resource scenarios.