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

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 wit...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-05, Vol.11 (10), p.2335
Main Authors: Zeng, Dehong, Chen, Xiaosong, Song, Zhengxin, Xue, Yun, Cai, Qianhua
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Computational linguistics
Data integration
Data mining
Deep learning
document-level multimodal sentiment classification
graph convolutional networks
Image enhancement
Language processing
Machine learning
Mathematics
Natural language interfaces
Neural networks
Product reviews
Semantics
Sentences
Sentiment analysis
Social networks
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11102335