###### Abstract
[25] proposed Low-rank Multimodal Fusion (LMF) to solve the problem of excessive complexity in tensor computation. [PERSON] et al. [26] introduced a modality-specific deconstruction method in the model to reduce information redundancy. [PERSON] et al. [27] proposed a regularization method to learn cross-time and cross-mode correlations in low-rank tensors. [PERSON] et al. [28] correlated features at the same time step and further proposed a dual low-order multimodal fusion method. [PERSON] et al. [29] used LSTM-based and tensor-based CNN networks to capture intra- and intermodal dynamic information encapsulated in asynchronous sequences. In recent years, a number of attention-based approaches have emerged. Through the attention mechanism, the model can be made to acquire inter- and intramodal correlations more efficiently. [PERSON] et al. [11] used attention units to capture dynamics across modalities. In [30; 31; 32; 33; 34], multihead and self-attention were used to perform cross-modal interactions, respectively, and perceive emotional information that is not within the modality. In addition, the researchers used other attention-based methods such as Gate Recursive Units (GRUs) [35; 36] and Graph Convolutional Networks (GCNs) [37]. Nevertheless, there are still two main challenges in current multimodal sentiment analysis research. The first one is inefficiency in modeling the intramodality and inter-modality dynamics. Multimodal sentiment analysis requires processing data from different modalities and correlating them to capture sentiment. It also needs to deal with sentiment dependencies within a single modality to help the model understand sentiment more accurately. The second one is the way in which different modal features are fused. Effective integration of features from different modalities can improve the accuracy and robustness of the model, which is crucial for the reliability of sentiment analysis in practical applications. In this paper, we use a transformer-based approach to capture sentiment information and extract dynamics within and between modalities, and we introduce the relevant coefficient for the fusion of multimodal features. In addition, we propose a new cross-correlation loss function for investigating the correlations between different levels of attention mechanisms. Specifically, we obtain the intermodality dynamics between the global representation and unimodal representation by using the cross-attention mechanism, which is the component of the Transformer, so that they can strengthen themselves by learning about each other in this process. At the same time, we obtain the intramodality information by using the self-attention mechanism for three unimodal features, respectively. In addition, in our research, we hypothesized that there is some correlation between different levels of attention mechanisms, so we propose the cross-correlation loss to assess the interrelationship between cross-attention and self-attention. The contributions of this paper can be summarized as follows:  
* We propose CCDA, a hierarchical model that studies intra- and intermodality correlations by using self-attention and cross-attention, respectively. Moreover, we introduce a new method to fuse multimodal features efficiently. * We innovatively introduce a new cross-correlation loss function to study the correlation between different levels of attentional mechanisms in more depth. The objective function is minimized to cut down redundant information, which can help our model to better perceive sentiment information. * Extensive experiments demonstrate the effectiveness of our proposed methodology. Our model achieves comparable results to the state-of-the-art (SOTA) approach in all evaluation metrics on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets.