## 5 Conclusions  
In this paper, we introduced the cross-correlation in dual-attention (CCDA) model aimed at fusing multimodal features and perceiving human sentiment analysis. We used dual-attention to obtain information about the intra- and intermodal dynamics contained in the samples from different perspectives, and in order to capture the relation that exists between different attention mechanisms, we propose the cross-correlation loss, which allows the cross-modal attention and the self-attention mechanism to complete a non-active interaction. In addition, we introduce a new fusion strategy in the multimodal feature fusion stage by using correlation coefficients, which allows the initial unimodal representation to guide the multimodal fusion. We conducted comprehensive experiments on three commonly used public datasets in the multimodal sentiment analysis domain, including CMU-MOSI, CMU-MOSEI, and CH-SIMS. We compared the CCDA model with baseline models and found that our model demonstrated a significant advantage on all three datasets. Through experimentation, we demonstrated the strong performance of the CCDA model in multimodal sentiment analysis tasks, offering new insights for further research and applications in this field. Since the Transformer is used in this study, an issue that cannot be ignored is the number of parameters of the model, which increases rapidly as the number of attention block increases. In addition, the cross-correlation loss as well as the relevant coefficients in this study were calculated using matrix multiplication, which increases the computational complexity of the model, and there is still some redundant information in the calculation process. Future research work:In view of the problems encountered in this study, future research efforts should focus on (1) reducing the number of parameters of the model while ensuring the model performance, (2) reducing the computational complexity of the model, and (3) further reducing the redundant information generated during the training process of the model. Given the challenges faced in real-world multimodal sentiment analysis, especially in scenarios involving missing modal information, future research could focus on enhancing the model's robustness and accuracy in handling missing modal information. This would ensure the effectiveness and reliability of the model in a wider range of practical applications. Figure 7: Impact of scaling weights in cross-correlation loss. Where a lower MAE (blue line) indicates better model performance, showing an opposite trend to the other metrics. **Author Contributions:** Conceptualization, P.W. ; methodology, P.W. ; software, P.W. ; validation, P.W., S.L., and J.C.; formal analysis, P.W. ; investigation, P.W. ; resources, P.W. ; data curation, P.W. ; writing--original draft preparation, P.W. ; writing--review and editing, P.W. ; visualization, P.W. ; supervision, P.W. ; project administration, P.W. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China (61762085) and the Natural Science Foundation of Xinjiang Uygur Autonomous Region Project (2019D01C081). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available in [[https://github.com/CMU-MultiComp-Lab/CMU-MultimodalSDK](https://github.com/CMU-MultiComp-Lab/CMU-MultimodalSDK)]([https://github.com/CMU-MultiComp-Lab/CMU-MultimodalSDK](https://github.com/CMU-MultiComp-Lab/CMU-MultimodalSDK)), reference number [21,46] and [[https://github.com/thuiar/MMSA](https://github.com/thuiar/MMSA)]([https://github.com/thuiar/MMSA](https://github.com/thuiar/MMSA)), reference number [47].