## 5 Conclusions  
Compared with existing models, the advantages of DCCMM model are as follows. The DCCMM model achieves dynamic coding at both the character level and the word level in the coding layer, integrating character-level and word-level information, and extracting more comprehensive information, making it better able to cope with the frequent occurrence of multiple meanings of words in microblog speech. Using the multi-scale convolutional kernel to extract local features in the text, DCCMM weakens the negative influence of irregularities in the language of microblog speech on the results of sentiment analysis. Cross-channel feature fusion and multi-head self-attention pooling are used to extract global semantic information and achieve key information filtering. Finally, the character-level and word-level semantic information is effectively fused and condensed through a multi-granularity interactive fusion mechanism. Therefore, DCCMM has a strong sentiment analysis capability and has an excellent ability to cope with the task of microblog remarks sentiment analysis. The DCCMM model achieves the best results in both the comparison and ablation experiments, indicating that the DCCMM can efficiently accomplish the task of microblog sentiment analysis.  
However, the sentiment analysis method proposed in this paper is only effective for textual information, and cannot address multimodal information such as pictures, videos, or voice. The information it can extract is still not sufficiently comprehensive.  
Therefore, in future work, our next plan is to modify the model structure by introducing operations such as convolution with strides and capsule modules to enable the model to handle multimodal information while reducing the parameters of the model, and to use the multimodal information for experiments to adjust the model structure and parameters in order to improve the usefulness of the model.  
Conceptualization, S.Y. and J.W.; writing--original draft, S.Y. and J.W.; methodology, S.Y. and Z.S.; software S.Y. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Major Projects of National Social Science Fund of China under Grant 20 AZD114, \"Kunpeng\" Research Fund Project of CCF-Green Alliance Technology under Grant CCF-NSFOCUS 2020011, and Public Safety Behavioral Sciences Laboratory Open Subject Fund Program of PPSUC under Grant 2020 SYS08.  
Not applicable.  
Data Availability Statement: Data available in a publicly accessible repository.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{4}{c}{**Weibo\_Senti\_100k**} & \multicolumn{4}{c}{**SMP2020-EWECT**} \\ \cline{2-9}  & **ACC (\%)** & **F1 (\%)** & **Time (min)** & **Epochs** & **ACC (\%)** & **F1 (\%)** & **TIME (min)** & **Epochs** \\ \hline Ablation experiment 1 & 98.32 & 98.32 & 66.32 & 4 & 79.41 & 74.45 & 26.57 & 5 \\ Ablation experiment 2 & 98.25 & 98.26 & 67.53 & 4 & 79.52 & 74.77 & 27.38 & 6 \\ Ablation experiment 3 & 98.36 & 98.34 & 66.92 & 4 & 79.56 & 74.83 & 26.48 & 6 \\ Ablation experiment 4 & 98.31 & 98.34 & 67.35 & 5 & 79.68 & 74.98 & 27.20 & 7 \\
**DCCMM** & **98.49** & **98.52** & **56.62** & **3** & **80.07** & **75.49** & **22.27** & **5** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation Experimental Model Performance Comparison.