Web Semantic-Enhanced Multimodal Sentiment Analysis Using Multilayer Cross-Attention Fusion

Abstract

Aiming at the existing multimodal sentiment analysis approaches, which include inadequate extraction of unimodal features, redundancy of independent modal features, insufficient analysis of semantic correlation between data and insufficient fusion, a Web-Semantic Enhanced Multimodal Sentiment Analysis Using Multilayer Cross-Attention Fusion is proposed. The model utilizes deep learning (including XLNet, ResNeSt, and convolutional neural networks) to extract high-level features from text, audio, and visual modes through self-attention mechanisms, and improves the accuracy of emotion classification through multimodal fusion. The results of experiments demonstrate that the suggested MCFMSA can achieve Acc-2, Acc-3, F1, and MAE values of 89.7%, 85.2%, 89.3%, and 0.466 on the CMU-MOSI dataset, respectively; and on the CMU-MOSEI dataset, Acc-2, Acc-3, F1, and MAE values of 88.7%, 82.5%, 86.5%, and 0.475. All of them are significantly improved compared to several other advanced multimodal sentiment analysis methods, which can enhance the accuracy of sentiment classification.