  In terms of human-computer interaction, it is becoming more and more
important to correctly understand the user's emotional state in a conversation,
so the task of multimodal emotion recognition (MER) started to receive more
attention. However, existing emotion classification methods usually perform
classification only once. Sentences are likely to be misclassified in a single
round of classification. Previous work usually ignores the similarities and
differences between different morphological features in the fusion process. To
address the above issues, we propose a two-stage emotion recognition model
based on graph contrastive learning (TS-GCL). First, we encode the original
dataset with different preprocessing modalities. Second, a graph contrastive
learning (GCL) strategy is introduced for these three modal data with other
structures to learn similarities and differences within and between modalities.
Finally, we use MLP twice to achieve the final emotion classification. This
staged classification method can help the model to better focus on different
levels of emotional information, thereby improving the performance of the
model. Extensive experiments show that TS-GCL has superior performance on
IEMOCAP and MELD datasets compared with previous methods.
