An Image Text Sentiment Analysis Method Using Bimodal Attention Mechanism

Aiming at the existing multimodal image sentiment analysis methods that suffer from insufficient text feature extraction, redundant independent modal features, insufficient analysis of semantic relationship between image and text data, and insufficient fusion, a multimodal image sentiment analysis method that integrates RoBERTa, Res-ViT and bimodal attention mechanism is proposed. By utilizing RoBERTa model to segment the input text, encode and extract high-level semantic features, local and global features of the image are obtained by using Res-ViT, and finally, image and text features are deeply fused to interact with each other by bimodal attention fusion. The model comprehensively mines the text and image features and deeply fuses the text–image features to interact, which enhances the accuracy of sentiment classification. The experimental outcomes demonstrate that the suggested RRMGA can enhance the accuracy of sentiment classification by attaining ACC and [Formula: see text] values of 0.7164 and 0.7042 on the MVSA-Multiple dataset, and 0.7519 and 0.7428 on the TumEmo dataset, respectively. These values are significantly higher than those of a number of other state-of-the-art multimodal image sentiment analysis approaches.