Abstract: Most multimodal sentiment analysis models utilize cross-modal attention mechanism to handle multimodal features.These approaches are prone to not only overlook the unique and effective information within each modality,but also suffer from the interference of redundant information shared across modalities,resulting in decreasing classification accuracy.To address this issue,this paper proposes a multimodal sentiment analysis model based on cross-modal unidirectional weighting.This model leverages a unidirectional weighting module to extract both shared and unique information within different modalities,and uses si-milar structure to interact between multimodal data.To prevent excessive extraction of repetitive information,it employs a KL divergence loss function for contrastive learning of identical modality information.Additionally,it introduces a gated temporal convolutional network with filtering function to extract features from unimodal data,thereby enhancing the expressive power of unimodal feature information.Evaluation on two public datasets,CMU-MOSI and CMU-MOSEI,against 13 baseline models show significant advantages in terms of classification accuracy,F1 score,and other metrics,validating the effectiveness of the proposed method.

Key words: Multimodal sentiment analysis, Transformer model, Unidirectional weighting, Attention mechanism, Kullback-Leibler divergence