The effectiveness of a model is heavily reliant on the quality of the fusion
representation of multiple modalities in multimodal sentiment analysis.
Moreover, each modality is extracted from raw input and integrated with the
rest to construct a multimodal representation. Although previous methods have
proposed multimodal representations and achieved promising results, most of
them focus on forming positive and negative pairs, neglecting the variation in
sentiment scores within the same class. Additionally, they fail to capture the
significance of unimodal representations in the fusion vector. To address these
limitations, we introduce a framework called Supervised Angular-based
Contrastive Learning for Multimodal Sentiment Analysis. This framework aims to
enhance discrimination and generalizability of the multimodal representation
and overcome biases in the fusion vector's modality. Our experimental results,
along with visualizations on two widely used datasets, demonstrate the
effectiveness of our approach.

通过引入一种名为监督角度对比学习的框架，以增强多模态表示的区分性和泛化性，克服了融合向量中的模态偏差，实验结果和对两个广泛使用数据集的可视化结果表明了我们的方法的有效性。