Abstract: With the increasing volume of data from social media platforms,multimodal aspect-level sentiment analysis is crucial for understanding the underlying emotions of users.Existing research primarily focuses on sentiment analysis tasks by fusing image and text modalities,but these methods fail to effectively capture the implicit emotions in both image and text.Furthermore,traditional approaches are often constrained by the black-box nature of the models,which lack interpretability.To address these issues,this paper proposes a confidence-guided prompt learning(CPL) based multimodal aspect-level sentiment analysis model,which consists of four key components:a multimodal feature processing module(MF),a confidence-based gating module(CG),a prompt construction module(PC),and a multimodal classification module(MC).The multimodal feature processing module is responsible for extracting features from multimodal data.The confidence-guided gating module evaluates the classification difficulty of samples using confidence assessment through a self-attention network and adaptively processes samples based on their difficulty.The prompt construction module generates adaptive prompt templates for different difficulty levels of samples to guide the T5 large language model in generating auxiliary sentiment cues.And the multimodal classification module is used for final sentiment prediction.Experimental results on the public datasets Twitter-2015 and Twitter-2017 show that,compared to existing baseline methods,the proposed multimodal aspect-level sentiment classification model achieves significant performance improvements,with accuracy increases of 0.48% and 1.06%,respectively.

Key words: Multimodal data, Large language models, Sentiment classification, Prompt learning, Classification confidence