Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

• URL: http://arxiv.org/abs/2208.09417v2
• Date: Mon, 24 Jul 2023 03:06:15 GMT
• Title: Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
• Authors: Yufeng Huang, Zhuo Chen, Jiaoyan Chen, Jeff Z. Pan, Zhen Yao, Wen Zhang
• Abstract summary: Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. Previous methods failed to account for the fine-grained semantic association between the image and the text. We propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs.
• Score: 27.77392307623526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene graphs to extract both global and local fine-grained image information and considers them as elements of the cross-modal semantic graph along with tokens from tweets. The sequential cross-modal semantic graph is represented as a sequence with a multi-modal adjacency matrix indicating relationships between elements. Experimental results show that the approach outperforms existing methods and achieves state-of-the-art performance on two standard datasets. Further analysis has demonstrated that the model can implicitly learn the correlation between fine-grained information of the image and the text with the given target. Our code is available at https://github.com/zjukg/SeqCSG.

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