## 2 Related Works
The tensor fusion network (TFN) [24] obtains the dynamic correlation between modes by calculating the outer product of bimodal and trimodal features. [PERSON] et al. [25] proposed a Low-rank Multimodal Fusion (LMF) method to solve the problem of excessive computational complexity in TFN, and utilized modality-specific low-rank factors for multimodal fusion to improve the efficiency. The Modality-based Redundancy Reduction Fusion (MRRF) [26] introduces a modal-specific decomposition method into the model, which removes redundant information from the dependency structure and leads to fewer parameters with minimal loss of information. [PERSON] et al. [27] proposed a regularization method to minimize the rank of the tensor and learn correlations across time and modes in low-rank tensors. [PERSON] et al. [28] correlated the features of a single time step between multiple modalities and further proposed a dual low-order multimodal fusion method to reduce computational complexity. [PERSON] et al. [29] used LSTM-based and tensor-based CNN networks to discover intra- and intermodal dynamics, and encapsulated them in an asynchronous sequence. However, tensor fusion is often accompanied by high-dimensional data representations, which, again, increases computational complexity while causing data sparsity. On the other hand, tensor fusion reduces the interpretability of the model, which may limit the credibility and acceptance of the model in practical applications. Attention mechanism (Especially Transformer [38], proposed by Google in 2017) plays a significant role in multimodal sentiment analysis; it helps models better understand and leverage the interconnections and semantic information between different modalities, and be more flexible and accurate in processing multimodal data. [PERSON] et al. [39] and [PERSON] et al. [11] used an LSTM-based model as well as attentional units to capture the dynamics across modalities. In [30; 31; 32; 33; 34], multihead and self-attention were used to capture relevant information within or across modalities. In addition, the researchers additionally used other methods, e.g., Gate Recurrent Unit (GRU) [35; 36] and Graph Convolutional Network (GCN) [37]. The Transformer exhibits strong generalization capabilities, making it suitable for different types of multimodal sentiment analysis tasks. In addition, there are other methods in multimodal sentiment analysis, such as multi-task contrastive learning [40], dynamic filtering mechanism [41], bidirectional multimodal dynamic routing mechanism [42], cross-modal hierarchical graph contrastive learning strategy [43], supervised contrastive learning [20; 44], dynamic refined sentiment words [45], etc. Previous studies have viewed modality self-attention and cross-modal attention as two separate units that cannot interact with each other. Therefore, in this study, we proposed Cross-Correlation in Dual-Attention model (CCDA) to capture the correlations that exist between the different attention layers, so that, after acquiring intra- and intermodal information, respectively, the model can also enable them to exchange information that is helpful for their respective learning. In addition, in the feature fusion stage, we propose a strategy to help the model converge quickly, by calculating the relevant coefficients between the unimodal self-attention features and the source feature representations to guide the multimodal feature fusion.