## 4 Results and Discussion
### Comparison Experiments and Results Analysis
#### 4.3.2 Comparison of Various Ensemble Models  
In order to prove that the MOGWO algorithm can effectively analyze and integrate the modeling capabilities of four base models, this paper compares the MOGWO algorithm with traditional Multi-Objective Particle Swarm Optimization (MOPSO) and NS-GAII algorithms to prove the practicability and advancement of the MOGWO algorithm. Table 4 shows the final recognition results obtained by different ensemble learning models. Figure 4 shows the curve of the variation of accuracy in the iterative process of different optimization algorithms. Based on Table 4 and Figure 4, the following conclusions can be drawn: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} } & \multicolumn{6}{c}{**Most States**} \\ \cline{3-10}  & & \begin{tabular}{c} **Great** \\ **Satisfaction** \\ \end{tabular} & \multicolumn{1}{c}{**Satisfaction**} & \multicolumn{1}{c}{**Neutral**} & \multicolumn{1}{c}{**Disappointment**} & \multicolumn{1}{c}{\begin{tabular}{c} **Great** \\ **Disappointment** \\ \end{tabular} } & \multirow{2}{*}{**Average**} \\ \hline \multirow{4}{*}{\begin{tabular}{c} The proposed \\ model \\ \end{tabular} } & Accuracy & 0.9760 & 0.9600 & 0.9700 & 0.9580 & 0.9720 & 0.9672 \\  & Precision & 0.9783 & 0.8846 & 0.9126 & 0.8692 & 0.9574 & 0.9204 \\  & Recall & 0.9000 & 0.9200 & 0.9400 & 0.9300 & 0.9000 & 0.9180 \\  & F1 & 0.9375 & 0.9020 & 0.9261 & 0.8986 & 0.9278 & 0.9184 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Word2 Vec \\ \end{tabular} } & Accuracy & 0.9740 & 0.9320 & 0.9480 & 0.9380 & 0.9600 & 0.9504 \\  & Precision & 0.9780 & 0.8000 & 0.8700 & 0.8286 & 0.9255 & 0.8804 \\  & Recall & 0.8900 & 0.8800 & 0.8700 & 0.8700 & 0.8700 & 0.8760 \\  & F1 & 0.9319 & 0.8381 & 0.8700 & 0.8488 & 0.8969 & 0.8771 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} GloVe \\ \end{tabular} } & Accuracy & 0.9620 & 0.9520 & 0.9420 & 0.9580 & 0.9580 & 0.9544 \\  & Precision & 0.9175 & 0.8585 & 0.8381 & 0.8990 & 0.9247 & 0.8876 \\  & Recall & 0.8900 & 0.9100 & 0.8800 & 0.8900 & 0.8600 & 0.8860 \\  & F1 & 0.9036 & 0.8835 & 0.8585 & 0.8945 & 0.8912 & 0.8862 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Word bag \\ model \\ \end{tabular} } & Accuracy & 0.9320 & 0.9260 & 0.9200 & 0.9260 & 0.9440 & 0.9296 \\  & Precision & 0.8511 & 0.8119 & 0.7830 & 0.8058 & 0.8750 & 0.8254 \\ \cline{1-1}  & Recall & 0.8000 & 0.8200 & 0.8300 & 0.8300 & 0.8400 & 0.8240 \\ \cline{1-1}  & F1 & 0.8247 & 0.8159 & 0.8058 & 0.8177 & 0.8571 & 0.8243 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Emotion recognition results of different word embedding models. Figure 3: Confusion matrixes of different word embedding models. 1. The ensemble learning model can effectively optimize the classification accuracy of a single model. This fully proves that ensemble learning is an effective method to improve the comprehensive recognition ability and generalization ability of models. The possible reason is that for different data, ensemble learning can dynamically optimize the weight according to the characteristics of different classifiers to establish the most suitable model for different data. 2. Compared with the traditional MOPSO algorithm and NSGAII algorithm, the MOGWO algorithm can achieve better results. This proves that the MOGWO algorithm has excellent weight optimization and decision-making ability in the field of integrated learning. The possible reason is that compared with the MOPSO algorithm and NSGAII algorithm, the MOGWO algorithm can obtain excellent optimization results with fewer parameters, which improves the overall convergence efficiency and global optimization ability of the model.