## 4 Experiment and Result Analysis
### Ablation Experiment Results and Analysis
#### 4.4.2 Multi-Head Self-Attention Pooling Ablation Experiment  
To verify the effect of multi-head self-attention pooling on the model's performance, three sets of ablation experiments were conducted in this paper. Except for the change in the multi-head self-attention pooling layer, all model parameters remained unchanged. The results of the multi-head self-attention pooling ablation experiment are shown in Table 7.  
\begin{table} \begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiment Number**} & \multicolumn{2}{c}{**Weibo\_Senti\_100k**} & \multicolumn{2}{c}{**SMP2020-EWECT**} \\ \cline{2-5}  & **ACC (\%)** & **F1 (\%)** & **ACC (\%)** & **F1 (\%)** \\ \hline Ablation experiment 1 & 97.26 & 97.25 & 75.68 & 70.08 \\ Ablation experiment 2 & 97.12 & 97.08 & 78.06 & 73.61 \\ Ablation experiment 3 & 97.33 & 97.32 & 78.63 & 74.21 \\ Ablation experiment 4 & 97.71 & 97.70 & 79.02 & 74.45 \\
**DCCMM** & **98.49** & **98.52** & **80.07** & **75.49** \\ \hline \hline \end{tabular}  Ablation experiment 1: Only the CLS vector in the ALBERT model is used to complete the microblog sentiment analysis task; i.e., the \(\mathbf{X_{C-CLS}}\) vector is dimensionally transformed and fed directly into the Softmax classifier to obtain the experimental results. Ablation experiment 2: Only the CLS vector in the WOBERT Plus model is used to complete the microblog sentiment analysis task, i.e., the \(\mathbf{X_{W-CLS}}\) vector is dimensionally transformed and fed directly into the Softmax classifier to obtain the experimental results. Ablation experiment 3: Only the word-level semantic information vector \(\mathbf{X_{W}}\) is used to complete the microblog sentiment analysis task, i.e., the vector \(\mathbf{X_{W}}\) is dimensionally transformed and fed directly into the Softmax classifier to obtain the experimental results. Ablation experiment 4: The fusion of \(\mathbf{X_{C-CLS}}\), \(\mathbf{X_{W-CLS}}\) and \(\mathbf{X_{W}}\) is performed using direct splicing, without going through the multi-granularity feature interaction fusion mechanism, which is fed into the Softmax classifier to derive the experimental result. The DCCMM model outperforms the other models in ablation experiments 1-3 on both datasets. On the dataset weibo_senti_100k, the accuracy and F1 score of the DCCMM model are improved by 1.23% and 1.27%, respectively, compared to the model in ablation experiment 1, by 1.37% and 1.44%, respectively, compared to the model in ablation experiment 2, and by 1.16% and 1.20%, respectively, compared to the model in ablation experiment 3. On the dataset SMP2020-EWECT, the accuracy and F1 score of the DCCMM model are improved by 4.39% and 5.41%, respectively, compared to the model in ablation experiment 1, by 2.01% and 1.88%, respectively, compared to the model in ablation experiment 2, and by 1.44% and 1.28%, respectively, compared to the model in ablation experiment 3. The result shows that only using character-level or word-level semantic information to complete the sentiment analysis task is ineffective, and using both character-level and word-level semantic information in combination to complement each other can improve model performance. The DCCMM model outperforms the model in ablation experiment 4 on both datasets. On the weibo_senti_100k dataset, the accuracy and F1 score of the DCCMM model are improved by 0.78% and 0.82%, respectively, compared to the model in ablation experiment 4. On the SMP2020-EWECT dataset, the accuracy and F1 score of the DCCMM model are improved by 1.05% and 1.04%, respectively, compared to the model in ablation experiment 4. This verifies the effectiveness of the proposed multi-granularity interactive fusion mechanism. Direct splicing of the character-level and word-level semantic information cannot realize the fusion between them effectively.