Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
This work improves sentiment classification for targets in multi-modal data, but it is incremental as it builds on existing encoder-decoder frameworks with enhanced graph-based methods.
The paper tackles the problem of multi-modal aspect-based sentiment classification by addressing the lack of fine-grained semantic association between images and text, proposing SeqCSG which uses sequential cross-modal semantic graphs to achieve state-of-the-art performance on two standard datasets.
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.