Hierarchical modality reconstruction and transformer interaction with adaptive fusion for multimodal sentiment analysis with missing modalities

Abstract Multimodal sentiment analysis (MSA) aims to recognize human emotions by integrating information from multiple sources, such as text, audio, and visual modalities. In recent years, this task has faced two major challenges in real-world applications. First, uncertain modality missingness leads to incomplete input information. Second, insufficient cross-modal interaction limits the effectiveness of multimodal fusion. Existing methods often treat reconstructed modalities and available modalities equally and lack adequate mechanisms for cross-modal interaction, which makes them prone to reconstruction errors and weakens the quality of multimodal fusion. To address these issues, we propose a novel framework for multimodal sentiment analysis with missing modalities, named AMF-MMSA (Attention Mechanisms and Missingness-Aware Fusion for Multimodal Sentiment Analysis with Missing Modalities). Under the complete-modality setting, AMF-MMSA first employs a self-attention mechanism to enhance modality-specific feature representations. When modality missing occurs, based on the enhanced features, we further design a modality reconstruction module (MR) guided by inter-modal affective consistency, while considering both random single-modality missing and multi-modality missing scenarios. Subsequently, all modality features, including reconstructed ones, are fed into a Transformer interaction module (TIM) to enable sufficient cross-modal interaction. Finally, a missingness-aware fusion module (MAF) explicitly distinguishes available modalities from reconstructed modalities and adaptively down-weights potentially low-quality reconstructed modalities, thereby achieving more robust multimodal fusion. Experimental results on three public datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS, demonstrate that under missing-rate settings of 0.0-0.5, AMF-MMSA significantly outperforms existing baseline methods across all evaluation metrics.