## 4 Experimental Research
### Ablation Analysis  
In order to verify the role of each component of the model and the integrity of the model, this paper strips each component from the model one by one and observes the degree of its impact on the model performance. If the model performance degrades after removing a component, it indicates that the component is indispensable and of independent importance. Otherwise, it is dispensable. If the model performance suffers when each component is removed, then the model is unified, and each component is indispensable.  
We conducted the following ablation experiments to verify the integrity of the RB_BG_MHA model. We removed each component one by one and observed changes in model performance. If the model's performance decreased after removing a component, it indicated that the component plays an essential role in the model's performance.  
Table 5 shows the ablation experiment results, which are visually represented as shown in Figure 6. The model was decomposed into three modules: sentence embedding, word embedding + BiGRU, and multi-head self-attention, and one module was removed one by one to evaluate its impact on the model performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Embedded Mode** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline RB & Sentence embedding & 81.058 & 80.563 & 81.886 & 81.219 \\ \hline RB\_MLP & Sentence embedding & 81.670 & 81.183 & 82.530 & 81.851 \\ \hline \multirow{3}{*}{RB\_BG} & Word embedding & 81.561 & 81.080 & 82.428 & 81.748 \\ \cline{2-6}  & Word embedding + Sentence embedding & 81.037 & 80.600 & 81.961 & 81.275 \\ \hline \multirow{3}{*}{RB\_BG\_MHA} & Word embedding & 81.863 & 81.237 & 82.481 & 81.854 \\ \cline{2-6}  & Word embedding + Sentence embedding & 82.931 & 82.257 & 83.436 & 82.842 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of the ablation study (RB: RoBERTa, MLP: Multi-Layer Perceptron, BG: Bidirectional Gated Recurrent Unit, MHA: Multi-Head Attention).  
Figure 6: Visual representation of ablation analysis (SE: Sentence Embedding, WE: Word Embedding, MHA: Multi-Head Attention).  
When the sentence embedding module was removed, the RB_BG_MHA model using only word embedding was formed, and the model's accuracy decreased by about 1.1%. The precision, recall, and F1-score all decreased by about 1.0%. When the multi-head self-attention module was removed, the RB_BG model using both word embedding and sentence embedding was formed, and the accuracy of the model was reduced by about 1.9%, the precision by about 1.7%, and the recall by about 1.5%. The F1-score fell by about 1.6%. When the word embedding module was removed, the RB_MLP model using only sentence embedding was formed, and the accuracy of the model decreased by about 1.3%, the precision decreased by about 1.1%, the recall decreased by about 0.9%, and the F1-score decreased by about 1.0%.  
Through the above experimental results, this paper can fully reveal the role of each component of the model and the integrity of the model, and it can be clearly observed that the performance of the RB-BG-MHA model depends on the joint action of each module, which is indispensable.