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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Multimodal sentiment analysis based on generative reconstruction and interactive self-mining in scenario of missing modalities

Feng Guanga
Zhou Kedonga
Wu Wenyanb
Huang Junhuib
Lin Yibaob
Liu Xintinga
Zhao Zhiwena
Su Xua
a. School of Automation, b. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0269

Abstract

In multimodal sentiment analysis tasks, real-world applications often face modality missing issues. Existing methods for missing modality generation heavily depend on automatically generated modality representations, which amplify generation errors and limit generalization ability. To address this, the Prompt-Reconstruct-Mining (PRM) framework is proposed. In both single-modal and dual-modal missing scenarios, the framework first uses generative prompts and available modality information to estimate the missing modality. It then designs a dual-modality-supported reconstruction mechanism that reduces single-source generation errors effectively. In the fusion phase, the framework introduces a Self-Mining Operator to explicitly learn deep semantic features from non-missing modalities. A Zero-Slot Insertion strategy aggregates global contextual information. Experimental results show that, in both single-modal and dual-modal missing scenarios on the CMU-MOSI and CMU-MOSEI datasets, the PRM model improves Accuracy and F1 by approximately 1%~3% on average. Moreover, the model demonstrates robust generalization ability in dynamic missing and cross-dataset transfer experiments, confirming its effectiveness and robustness in complex missing scenarios.

Foundation Support

国家自然科学基金重点项目(62237001)
广东工业大学教育信息化教改专项"人工智能背景下的高校数据治理实施路径研究"(211230073)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0269
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-18] Accepted Paper

Cite This Article

冯广, 周科栋, 伍文燕, 等. 模态缺失场景下基于生成重构和交互式自挖掘的多模态情感分析 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0269. (Feng Guang, Zhou Kedong, Wu Wenyan, et al. Multimodal sentiment analysis based on generative reconstruction and interactive self-mining in scenario of missing modalities [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0269. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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