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Compared with unimodal data, multimodal data can provide more features tohelp the model analyze the sentiment of data. Previous research works rarelyconsider token-level feature fusion, and few works explore learning the commonfeatures related to sentiment in multimodal data to help the model fusemultimodal features. In this paper, we propose a Contrastive Learning andMulti-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-levelfeatures of text and image. In addition to the sentiment analysis task, we alsodesigned two contrastive learning tasks, label based contrastive learning anddata based contrastive learning tasks, which will help the model learn commonfeatures related to sentiment in multimodal data. Extensive experimentsconducted on three publicly available multimodal datasets demonstrate theeffectiveness of our approach for multimodal sentiment detection compared withexisting methods. The codes are available for use athttps://github.com/Link-Li/CLMLF

CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection