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Multimodal sentiment analysis has become an increasingly popular researcharea as the demand for multimodal online content is growing. For multimodalsentiment analysis, words can have different meanings depending on thelinguistic context and non-verbal information, so it is crucial to understandthe meaning of the words accordingly. In addition, the word meanings should beinterpreted within the whole utterance context that includes nonverbalinformation. In this paper, we present a Context-driven Modality Shifting BERTwith Contrastive Learning for linguistic, visual, acoustic Representations(CMSBERT-CLR), which incorporates the whole context's non-verbal and verbalinformation and aligns modalities more effectively through contrastivelearning. First, we introduce a Context-driven Modality Shifting (CMS) toincorporate the non-verbal and verbal information within the whole context ofthe sentence utterance. Then, for improving the alignment of differentmodalities within a common embedding space, we apply contrastive learning.Furthermore, we use an exponential moving average parameter and label smoothingas optimization strategies, which can make the convergence of the network morestable and increase the flexibility of the alignment. In our experiments, wedemonstrate that our approach achieves state-of-the-art results.

CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations