Text-Centric Multimodal Contrastive Learning for Sentiment Analysis

Multimodal sentiment analysis aims to acquire and integrate sentimental cues from different modalities to identify the sentiment expressed in multimodal data. Despite the widespread adoption of pre-trained language models in recent years to enhance model performance, current research in multimodal sentiment analysis still faces several challenges. Firstly, although pre-trained language models have significantly elevated the density and quality of text features, the present models adhere to a balanced design strategy that lacks a concentrated focus on textual content. Secondly, prevalent feature fusion methods often hinge on spatial consistency assumptions, neglecting essential information about modality interactions and sample relationships within the feature space. In order to surmount these challenges, we propose a text-centric multimodal contrastive learning framework (TCMCL). This framework centers around text and augments text features separately from audio and visual perspectives. In order to effectively learn feature space information from different cross-modal augmented text features, we devised two contrastive learning tasks based on instance prediction and sentiment polarity; this promotes implicit multimodal fusion and obtains more abstract and stable sentiment representations. Our model demonstrates performance that surpasses the current state-of-the-art methods on both the CMU-MOSI and CMU-MOSEI datasets.