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