{"ID":2838460,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16990","arxiv_id":"2511.16990","title":"Senti-iFusion: An Integrity-centered Hierarchical Fusion Framework for Multimodal Sentiment Analysis under Uncertain Modality Missingness","abstract":"Multimodal Sentiment Analysis (MSA) is critical for human-computer interaction but faces challenges when the modalities are incomplete or missing. Existing methods often assume pre-defined missing modalities or fixed missing rates, limiting their real-world applicability. To address this challenge, we propose Senti-iFusion, an integrity-centered hierarchical fusion framework capable of handling both inter- and intra-modality missingness simultaneously. It comprises three hierarchical components: Integrity Estimation, Integrity-weighted Completion, and Integrity-guided Fusion. First, the Integrity Estimation module predicts the completeness of each modality and mitigates the noise caused by incomplete data. Second, the Integrity-weighted Cross-modal Completion module employs a novel weighting mechanism to disentangle consistent semantic structures from modality-specific representations, enabling the precise recovery of sentiment-related features across language, acoustic, and visual modalities. To ensure consistency in reconstruction, a dual-depth validation with semantic- and feature-level losses ensures consistent reconstruction at both fine-grained (low-level) and semantic (high-level) scales. Finally, the Integrity-guided Adaptive Fusion mechanism dynamically selects the dominant modality for attention-based fusion, ensuring that the most reliable modality, based on completeness and quality, contributes more significantly to the final prediction. Senti-iFusion employs a progressive training approach to ensure stable convergence. Experimental results on popular MSA datasets demonstrate that Senti-iFusion outperforms existing methods, particularly in fine-grained sentiment analysis tasks. The code and our proposed Senti-iFusion model will be publicly available.","short_abstract":"Multimodal Sentiment Analysis (MSA) is critical for human-computer interaction but faces challenges when the modalities are incomplete or missing. Existing methods often assume pre-defined missing modalities or fixed missing rates, limiting their real-world applicability. To address this challenge, we propose Senti-iFu...","url_abs":"https://arxiv.org/abs/2511.16990","url_pdf":"https://arxiv.org/pdf/2511.16990v1","authors":"[\"Liling Li\",\"Guoyang Xu\",\"Xiongri Shen\",\"Zhifei Xu\",\"Yanbo Zhang\",\"Zhiguo Zhang\",\"Zhenxi Song\"]","published":"2025-11-21T06:54:21Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}