Abstract: Multimodal sentiment analysis has emerged as one of the most prominent tasks in the multimodal learning domain,aiming to predict emotions by leveraging the complementary strengths of different modalities.Due to the significant heterogeneity among different modalities in terms of temporal structure,semantic space,and representational scales,achieving deep and consis-tent alignment remains inherently challenging,which impedes the full exploitation of affective features from audio and visual modalities.Moreover,most existing models adopt simple concatenation or weighted summation mechanisms during the fusion stage,lacking dynamic modeling and selective filtering of critical affective cues, thereby resulting in limited fusion expressiveness.To address these challenges,this paper proposes TPGF(Text-Guided Prompt and Gated Fusion Model),a novel multimodal sentiment analysis framework that integrates text-guided prompt learning and gated fusion mechanisms.Specifically,to mitigate modality heterogeneity,TPGF introduces a prompt learning module during cross-modal semantic interaction,leveraging the dominant semantic information from text to guide the alignment and fusion of audio and visual modalities.To overcome the limitations of simplistic fusion,a hierarchical cross-modal gated fusion mechanism is designed that performs dynamic selection and integration of salient features.Furthermore,semantic augmentation strategies for both text and audio modalities are introduced to enhance the robustness and expressive power of the model.Extensive experiments on two benchmark datasets,CMU-MOSI and CMU-MOSEI,demonstrate that TPGF significantly outperforms state-of-the-art models in terms of classification accuracy,F1 score,and other evaluation metrics,validating the effectiveness and superiority of the proposed framework.

Key words: Multimodal learning, Prompt learning, Gated fusion, Semantic enhancement, Sentiment analysis