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Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews

Author

Listed:
  • Dehong Zeng

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China)

  • Xiaosong Chen

    (School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China)

  • Zhengxin Song

    (School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China)

  • Yun Xue

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China)

  • Qianhua Cai

    (School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China)

Abstract

An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network to deal with both texts and images on the task of document-level multimodal sentiment analysis. The image caption is introduced as an auxiliary, which is aligned with the image to enhance the semantics delivery. Then, a graph is constructed with the sentences and images generated as nodes. In line with the graph learning, the long-distance dependencies can be captured while the visual noise can be filtered. Specifically, a cross-modal graph convolutional network is built for multimodal information fusion. Extensive experiments are conducted on a multimodal dataset from Yelp. Experimental results reveal that our model obtains a satisfying working performance in DLMSA tasks.

Suggested Citation

  • Dehong Zeng & Xiaosong Chen & Zhengxin Song & Yun Xue & Qianhua Cai, 2023. "Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews," Mathematics, MDPI, vol. 11(10), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2335-:d:1148742
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    References listed on IDEAS

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    1. Haoliang Xiong & Zehao Yan & Hongya Zhao & Zhenhua Huang & Yun Xue, 2022. "Triplet Contrastive Learning for Aspect Level Sentiment Classification," Mathematics, MDPI, vol. 10(21), pages 1-14, November.
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    Cited by:

    1. Han Ma & Baoyu Fan & Benjamin K. Ng & Chan-Tong Lam, 2024. "VL-Meta: Vision-Language Models for Multimodal Meta-Learning," Mathematics, MDPI, vol. 12(2), pages 1-16, January.

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