CLAP: Contrastive Language-Audio Pre-training Model for Multi-modal Sentiment Analysis
Tingzhen Zhao, Ming Kong, Tian Liang, Qiang Zhu, Kun Kuang, Fei Wu
Abstract
Multi-modal Sentiment Analysis (MSA) is a hotspot of multi-modal fusion. To make full use of the correlation and complementarity between modalities in the process of fusing multi-modal data, we propose a two-stage framework of Contrastive Language-Audio Pre-training (CLAP) for the MSA task: 1) Making contrastive pre-training on an unlabeled large-scaled external data to yield better single-modal representations; 2) Adopting a Transformer-based multi-modal fusion module, to achieve further single-modal feature optimization and sentiment prediction via the task-driven training process. Our work fully demonstrates the importance and necessity of core elements such as pre-training, contrastive learning, and representation learning for the MSA task and significantly outperforms existing methods on two well-recognized MSA benchmarks.