Ensuring Pre-Fusion Modality Consistency: A New Approach to Multimodal Sentiment Detection

With the growing diversity of data formats on social media, such as text, images, and videos, there is a growing need to analyze sentiment from multiple modalities. Multimodal sentiment detection, which aims to identify users’ sentiment by jointly modeling information from different modalities, has thus attracted increasing attention. However, most existing multimodal sentiment detection methods fuse multimodal information directly after the unimodal encoding and overlook the modality consistency of multimodal vector spaces before the fusion, which may damage the accuracy of multimodal sentiment detection. To address this issue, we propose a contrastive learning-based multimodal sentiment detection model termed EPMC which can map the representations of different modalities into a unified semantic space before fusion. EPMC operates in two stages, i.e., pre-training stage and fine-tuning stage. At the pre-training stage, we designed a cross-modal transformation module to map different modalities into a unified feature space. Meanwhile, to further capture the relationship between the cross-modal transformation vectors and the unimodal encoding vectors, we propose a multimodal consistency contrastive learning task that helps the model discern and amplify the cross-modal similarity between different modalities, thereby learning more discriminative features for sentiment detection. At the fine-tuning stage, EPMC is iteratively refined using the learned multimodal representation and guided by the cross-entropy loss. Extensive experiments conducted on three public multimodal datasets validate the effectiveness of EPMC model. The official implementation of EPMC is released at https://github.com/ADMIS-TONGJI/EPMC .