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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Multimodal aspect-level sentiment analysis based on cross-modal interaction Transformer

Gan Zhuohao1a
Miao Yuqing1a,1b,1c
Liu Tonglai2
Zhang Wanzhen2
Zhou Ming3
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. College of Information Science & Technology, Zhongkai University of Agriculture & Engineering, Guangzhou 510225, China
3. Guilin Hivision Technology Company, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0517

Abstract

To address the issues of insufficient visual information extraction and the lack of aspect sentiment semantics in existing multimodal aspect-level sentiment analysis models, a multimodal aspect-level sentiment analysis model based on Cross-Modal Interaction Transformer (CMIT) is proposed. The model uses a text semantic enhancement module to integrate image captions with the original text, compensating for missing sentiment semantics. It constructs an aspect-aware feature extraction module using dependency syntax analysis and graph convolutional networks (GCN) to capture long-distance dependencies between aspect terms and opinion words. A cross-modal feature interaction module is designed, combining a TOP-n adjective-noun pair distribution constraint strategy and a multimodal fusion Transformer to achieve deep interaction between image and text features. Experimental results on the Twitter-2015, Twitter-2017, and ZOL datasets demonstrate that the CMIT model outperforms several baseline models in both accuracy and macro-average F1 score, validating its effectiveness and generalization ability.

Foundation Support

国家自然科学基金资助项目(62366010,62366011)
广东省研究生教育创新计划项目(2022JGXM115)
教育部产学合作协同育人项目(202102191071,202102211021)
桂林电子科技大学研究生教育创新计划资助项目(2024YCXS068)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0517
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-25] Accepted Paper

Cite This Article

甘卓浩, 缪裕青, 刘同来, 等. 基于跨模态交互Transformer的多模态方面级情感分析 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0517. (Gan Zhuohao, Miao Yuqing, Liu Tonglai, et al. Multimodal aspect-level sentiment analysis based on cross-modal interaction Transformer [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0517. )

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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