  Multimodal sentiment analysis is an active research area that combines
multiple data modalities, e.g., text, image and audio, to analyze human
emotions and benefits a variety of applications. Existing multimodal sentiment
analysis methods can be classified as modality interaction-based methods,
modality transformation-based methods and modality similarity-based methods.
However, most of these methods highly rely on the strong correlations between
modalities, and cannot fully uncover and utilize the correlations between
modalities to enhance sentiment analysis. Therefore, these methods usually
achieve bad performance for identifying the sentiment of multimodal data with
weak correlations. To address this issue, we proposed a two-stage
semi-supervised model termed Correlation-aware Multimodal Transformer (CorMulT)
which consists pre-training stage and prediction stage. At the pre-training
stage, a modality correlation contrastive learning module is designed to
efficiently learn modality correlation coefficients between different
modalities. At the prediction stage, the learned correlation coefficients are
fused with modality representations to make the sentiment prediction. According
to the experiments on the popular multimodal dataset CMU-MOSEI, CorMulT
obviously surpasses state-of-the-art multimodal sentiment analysis methods.
