App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Prompt-learning-based multimodal sentiment analysis under missing modalities

Zheng Mingzhou1a
Miao Yuqing1a,1b
Liu Tonglai2
Zhang Wanzhen2
Cai Guoyong1a,1b,1c
1. a. School of Computer Science & Information Security, b. Guangxi Key Laboratory of Image & Graphics Intelligent Processing, c. Guangxi Key Laboratory of Cryptography & Information Security, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
2. School of Artificial Intelligence, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
DOI: 10.19734/j.issn.1001-3695.2025.10.0430

Abstract

To address the issues in multimodal sentiment analysis under missing modalities—such as the complex fine-tuning of Transformer models, the indiscriminate use of completed modality data, and the weakened information complementarity caused by modality missing—this paper proposed a prompt-learning-based multimodal sentiment analysis model under missing modalities. The model first introduced modality-missing prompts to guide the model in identifying whether the current input modality is missing. Secondly, a quality evaluation mechanism was constructed to suppress the interference of low-quality completed modality data on the model. On this basis, modality-missing combination prompts were designed and added into the Transformer of the backbone network to guide the model to dynamically adjust attention computation and cross-modal interaction, improving the model’s adaptability to missing-modality scenarios, while avoiding complex fine-tuning of the Transformer backbone and reducing computational cost. Finally, a shared fusion layer was added to map the features of each modality into a unified shared representation space, learning shared semantic information and enhancing cross-modal semantic consistency and information complementarity. Experimental results show that, under six missing-modality combinations, the model improve the average Acc-2 and F1 scores on the CMU-MOSI, IEMOCAP, and CH-SIMS datasets by 1.04%-1.87% compared with the second-best model. In addition, the trainable parameters account for only 6.3% of the total parameters, verifying the effectiveness, robustness, and parameter efficiency of the proposed model.

Foundation Support

国家自然科学基金资助项目(62366010,62366011)
广东省自然科学基金资助项目(2023A1515011230)
桂林电子科技大学研究生教育创新计划资助项目(2025YCXS076)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.10.0430
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-26] Accepted Paper

Cite This Article

郑明洲, 缪裕青, 刘同来, 等. 模态缺失下基于提示学习的多模态情感分析 [J]. 计算机应用研究, 2026, 43 (6). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0430. (Zheng Mingzhou, Miao Yuqing, Liu Tonglai, et al. Prompt-learning-based multimodal sentiment analysis under missing modalities [J]. Application Research of Computers, 2026, 43 (6). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.10.0430. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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