  In this paper, we propose a new Multimodal Representation Learning (MRL)
method for Multimodal Sentiment Analysis (MSA), which facilitates the adaptive
interaction between modalities through Cooperative Sentiment Agents, named
Co-SA. Co-SA comprises two critical components: the Sentiment Agents
Establishment (SAE) phase and the Sentiment Agents Cooperation (SAC) phase.
During the SAE phase, each sentiment agent deals with an unimodal signal and
highlights explicit dynamic sentiment variations within the modality via the
Modality-Sentiment Disentanglement (MSD) and Deep Phase Space Reconstruction
(DPSR) modules. Subsequently, in the SAC phase, Co-SA meticulously designs
task-specific interaction mechanisms for sentiment agents so that coordinating
multimodal signals to learn the joint representation. Specifically, Co-SA
equips an independent policy model for each sentiment agent that captures
significant properties within the modality. These policies are optimized
mutually through the unified reward adaptive to downstream tasks. Benefitting
from the rewarding mechanism, Co-SA transcends the limitation of pre-defined
fusion modes and adaptively captures unimodal properties for MRL in the
multimodal interaction setting. To demonstrate the effectiveness of Co-SA, we
apply it to address Multimodal Sentiment Analysis (MSA) and Multimodal Emotion
Recognition (MER) tasks. Our comprehensive experimental results demonstrate
that Co-SA excels at discovering diverse cross-modal features, encompassing
both common and complementary aspects. The code can be available at
https://github.com/smwanghhh/Co-SA.
