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

Dehong Zeng, Xiaosong Chen, Zhengxin Song, Yun Xue and Qianhua Cai ()
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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

Mathematics, 2023, vol. 11, issue 10, 1-16

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.

Keywords: document-level multimodal sentiment classification; graph convolutional networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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