Jieyu An1,*, Wan Mohd Nazmee Wan Zainon1, Binfen Ding2
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1673-1689, 2023, DOI:10.32604/iasc.2023.039763
Abstract Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses the power of transfer learning… More >
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