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Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
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Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: NAACL-HLT (1). pp. 380–385. Association for Computational Linguistics (2019)
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Yu, J., Jiang, J., Xia, R.: Entity-sensitive attention and fusion network for entity-level multimodal sentiment classification. IEEE ACM Trans. Audio Speech Lang. Process. 28
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Dai, J., Yan, H., Sun, T., Liu, P., Qiu, X.: Does syntax matter? A strong baseline for aspect-based sentiment analysis with roberta. In: NAACL-HLT. pp. 1816–1829. Association for Computational Linguistics (2021)
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2022
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Ling, Y., Yu, J., Xia, R.: Vision-language pre-training for multimodal aspect-based sentiment analysis. In: ACL (1). pp. 2149–2159. Association for Computational Linguistics (2022)
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Yu, J., Wang, J., Xia, R., Li, J.: Targeted multimodal sentiment classification based on coarse-to-fine grained image-target matching. In: IJCAI. pp. 4482–4488. ijcai.org (2022)
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