## 4 Results and Discussion
### Comparison Experiments and Results Analysis
#### 4.3.3 Comparison of Other Existing Models  
In order to fully prove that the model proposed has excellent application value and is cutting edge, this paper compares the proposed model with five common benchmark models, including Seq2 seq-Attention, Bilstm, RNN, TextCNN, and SVM. In these benchmark models, common text analysis models and some frontier models are included. Figure 5 shows the confusion matrix of the classification results of the proposed model. Figure 6 shows the ROC curves of the five emotions of the proposed model. Table 5 shows the AUC values of five emotions of the model proposed in this paper. Table 6 shows the emotion recognition results of all models. Based on Tables 5 and 6 and Figures 5 and 6, the following conclusions can be drawn:  
1. Compared with traditional machine learning models, other deep learning models can achieve more excellent classification results. This fully proves that the deep learning algorithm has an excellent performance in the field of text emotion recognition. The possible reason is that the deep learning model effectively extracts the features of the original text through the multi-layer neural network structure and achieves more excellent results.
2. Compared with the traditional Bilstm neural network, the SEq2 SEq_attention algorithm can obtain more accurate recognition results. This effectively proves that the attention mechanism effectively optimizes the performance of the neural network. The possible reason is that the attention mechanism can extract the deep correlation between the word vector data more deeply, which effectively improves the overall analysis and modeling ability of the model and achieves better recognition results.
3. The ensemble method proposed in this paper can achieve the best recognition results, which fully proves that the model proposed in this paper has excellent classification performance. The ensemble model adopted in this paper fully combines the advantages of each component. On the one hand, the feature representation of the original word vector is extracted by using the Word2 Vec and GloVe word embedding models trained in this model, which effectively reduces the sparsity of the data. On the other hand, convolutional neural network and long and short-term memory networks were used for feature training, respectively, and four submodels were obtained by combining the attention mechanism. Finally, the integrated learning model based on a multi-objective gray Wolf optimizer can effectively make decisions on the weight of each sub-model and obtain the final emotion analysis results. Therefore, the model adopted in this paper can achieve excellent results in the field of emotion recognition.
4. Based on the results of AUC and ROC, it can be seen that the model proposed in this paper can obtain relatively accurate classification results for different emotion categories. In addition, the average AUC values for all classes of the model are satisfactory. Therefore, the model proposed in this paper has excellent emotional recognition ability and can stably identify different emotional categories.  
Figure 5: Confusion matrixes of the proposed model and other existing models.  
Figure 6: ROC curves of the proposed model.