  Compared with unimodal data, multimodal data can provide more features to
help the model analyze the sentiment of data. Previous research works rarely
consider token-level feature fusion, and few works explore learning the common
features related to sentiment in multimodal data to help the model fuse
multimodal features. In this paper, we propose a Contrastive Learning and
Multi-Layer Fusion (CLMLF) method for multimodal sentiment detection.
Specifically, we first encode text and image to obtain hidden representations,
and then use a multi-layer fusion module to align and fuse the token-level
features of text and image. In addition to the sentiment analysis task, we also
designed two contrastive learning tasks, label based contrastive learning and
data based contrastive learning tasks, which will help the model learn common
features related to sentiment in multimodal data. Extensive experiments
conducted on three publicly available multimodal datasets demonstrate the
effectiveness of our approach for multimodal sentiment detection compared with
existing methods. The codes are available for use at
https://github.com/Link-Li/CLMLF
