###### Abstract  
With the development of the Internet, the content that people share contains types of text, images, and videos, and utilizing these multimodal data for sentiment analysis has become an important area of research. Multimodal sentiment analysis aims to understand and perceive emotions or sentiments in different types of data. Currently, the realm of multimodal sentiment analysis faces various challenges, with a major emphasis on addressing two key issues: (1) inefficiency when modeling the intramodality and intermodality dynamics and (2) inability to effectively fuse multimodal features. In this paper, we propose the CCDA (cross-correlation in dual-attention) model, a novel method to explore dynamics between different modalities and fuse multimodal features efficiently. We capture dynamics at intra- and intermodal levels by using two types of attention mechanisms simultaneously. Meanwhile, the cross-correlation loss is introduced to capture the correlation between attention mechanisms. Moreover, the relevant coefficient is proposed to integrate multimodal features effectively. Extensive experiments were conducted on three publicly available datasets, CMU-MOSI, CMU-MOSEI, and CH-SIMS. The experimental results fully confirm the effectiveness of our proposed method, and, compared with the current optimal method (SOTA), our model shows obvious advantages in most of the key metrics, proving its better performance in multimodal sentiment analysis. multimodality; sentiment analysis; attention mechanism +
Footnote †: journal:  
each modality and then predicting sentiment according to the weight of the classifier's results. For example, [PERSON] et al. [14] used Kalman filters as combiners for decision-making. [PERSON] et al. [15] first used several different CNNs and subsequently vectorized and fused the output of the features from each CNN. [PERSON] et al. [16] used Sequential Minimal Optimization (SMO, a variant of SVM) with different kernel functions and fused their results in decision-making. Although these two methods were relatively simple, when dealing with modal features, the model is unable to capture intra- and intermodality dynamics efficiently, which may lead to poor model performance. The researchers then combined the advantages of early and late fusion and proposed hybrid fusion. [PERSON] et al. [17] used deep CNNs to extract features and fused multimodal features using MKL and determine the weights of textual modalities using a decision fusion approach in the final stage. [PERSON] et al. [18] used gating mechanisms to selectively learn cross-modal interaction information and used the results for sentiment prediction. [PERSON] et al. [19] used a multihead attention mechanism to extract semantic and sentiment analysis, then train multiple base classifiers and ultimately fuse the decisions of the base classifiers. Word-level fusion fuses different modalities in a temporal step to obtain cross-modal correlations. For example, [PERSON] et al. [20] proposed a memory fusion network (MFN), by simulating interactions within modalities and generalizing the temporal relationships between different modalities, the sequence is ultimately unified based on the relationships between unimodal word-level features. Subsequently, in [21], they proposed a Graph-Memory Fusion Network and performed word-level fusion by using a dynamic fusion graph. [PERSON] et al. [22] proposed an LSTHM-based model to obtain cross-modal interactions by performing a multi-stage fusion of modalities features between each time step. [PERSON] et al. [23] proposed a Recurrent Attended Variation Embedding Network (RAVEN), by modeling the fine-grained structure in word segments and transforming word representations based on nonverbal dynamic information. Tensor fusion uses different tensor-based computation methods to allow different modalities to interact. [PERSON] et al. [24] proposed Tensor Fusion Network (TFN), modal correlations are obtained by computing the outer product between the feature tensors. [PERSON] et al.