TMFN: a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning for multimodal sentiment analysis

Multimodal sentiment analysis (MSA) is crucial in human-computer interaction. Current methods use simple sub-models for feature extraction, neglecting multi-scale features and the complexity of emotions. Text, visual, and audio each have unique characteristics in MSA, with text often providing more emotional cues due to its rich semantics. However, current approaches treat modalities equally, not maximizing text’s advantages. To solve these problems, we propose a novel method named a text-based multimodal fusion network with multi-scale feature extraction and unsupervised contrastive learning (TMFN). Firstly, we propose an innovative pyramid-structured multi-scale feature extraction method, which captures the multi-scale features of modal data through convolution kernels of different sizes and strengthens key features through channel attention mechanism. Second, we design a text-based multimodal feature fusion module, which consists of a text gating unit (TGU) and a text-based channel-wise attention transformer (TCAT). TGU is responsible for guiding and regulating the fusion process of other modal information, while TCAT improves the model’s ability to capture the relationship between features of different modalities and achieves effective feature interaction. Finally, to further optimize the representation of fused features, we introduce unsupervised contrastive learning to deeply explore the intrinsic connection between multi-scale features and fused features. Experimental results show that our proposed model outperforms the state-of-the-art models in MSA on two benchmark datasets.