## 4 Experiment and Result Analysis
### Experimental Parameter and Environment Settings  
The convolution kernel sizes of the convolution layer are 2, 3, and 4, respectively. The number of convolution kernels of each size is 256. The number of heads in the multi-head attention mechanism is 8. The batch size is 16. The epoch is 10. The learning rate is \(1\times 10^{-5}\), and dropout is 0.3. The experimental parameter setting is shown in Table 2. The experimental environment is shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Item** & **Weibo\_Senti\_100k** & **SMP2020-EWECT** \\ \hline Number of emotion categories & 2 & 6 \\ Total amount of data & 119,988 & 48,374 \\ Training set & 95,990 & 38,699 \\ Validation set & 11,999 & - \\ Test set & 11,999 & 9675 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset information. 1. TextCNN [32]: Local key features are extracted by convolutional operation, and obvious features are filtered by max-pooling. Then, this information is used for classification. 2. Bi-LSTM [33]: A bidirectional LSTM is used to extract contextual semantic and sequential information from the text. 3. Bi-LSTM-Attention [34]: Based on the above Bi-LSTM model, the attention mechanism is added to enable the model to adjust the weight parameters according to the different levels of data importance and increase the weight of key information. 4. RCNN [35]: Global semantic information is first extracted using Bi-LSTM, and obvious features are extracted using max-pooling. Then, this information is used for classification. 5. ALBERT [19]: The feature vector corresponding to the position of the ALBERT model (CLS) is used for sentence representation. 6. WOBERT Plus [24]: The feature vector corresponding to the position of the WOBERT Plus model [CLS] is used for sentence representation. 7. BERT-Bi-LSTM-Attention [36]: Dynamic character-level vectors are generated using BERT, followed by the Bi-LSTM network to extract features, and then feature optimization is performed using the attention mechanism. 8. BERT-HAN [37]: The model proposed in [37]. 9. BERT + CFEBLS [38]: The model proposed in [38]. 10. MDMLSM [39]: The model proposed in [39]. 11. ACL-RoBERTa-CNN [40]: The model proposed in [40]. 12. CBMA [41]: The model proposed in [41]. 13. CTBERT [42]: The model proposed in [42]. 14. DCCMM: The model proposed in this paper. On the basis of a comparison of the experimental results, it can be seen that the DCCMM model achieves the highest accuracy and F1 values on each dataset, outperforming the mainstream deep learning models and the latest sentiment analysis models. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. This fully verifies the advanced and effective performance of the DCCMM model for the microblog sentiment analysis task. 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.