## 4 Results
### Results and Analysis
#### 4.3.2 Comparative Analysis of Experimental Results
To study the effects of different components, we performed an ablation experiment, removing one component from the DESG at a time. DESG-MLE: DESG does not use an emotion classifier. DESG-EV: DESG uses external emotional words, not internal emotional words. DESG-BS: DESG uses a cluster search algorithm instead of our diverse sorting algorithm. DESG-IM: DESG does not use the internal emotion regulator. The comparison results of ablation experiments are shown in Table 5:  
As shown in the comparison results of the ablation experiments in Table 5, after removing the emotion classifier component, the DESG-MLE model had a significant decline in its emotional performance index. Because there was no emotion classifier present, the DESG-MLE model could only clearly express the target emotion in the generated response, and could not capture the emotion sequences that did not contain any emotional words. For the DESG-EV model, the internal emotion dictionary was removed and the external emotion dictionary was used instead, leading to a significant decrease in its emotional performance index. This is because the external emotion dictionary and corpus share fewer words, causing the generation process to focus on generic vocabulary and more commonplace responses to be generated. The DESG-BS model adopted the cluster search algorithm without utilizing the diversified sorting algorithm, leading to a significant decline in its response diversity and proving that the diversity sorting algorithm applied in out model could promote a diversity within generated responses. The DESG-IM model without an internal emotion regulator showed a significant reduction in terms of the BLEU evaluation index, emotional performance, and diversity of responses, thus proving that an internal emotion regulator can not only guarantee the grammatical correctness of a generated response but also dynamically balance the weight between grammar and emotion. In Table 6, a sample response to a single post is listed corresponding to each emotion category. The responses containing emotional words (highlighted in a blue font) were expressed explicitly, while those without were expressed implicitly. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **BLEU** & \multicolumn{2}{c}{**Word Vector-Based Method**} & \multicolumn{2}{c}{**Emotional Performance**} & \multicolumn{2}{c}{**Responses Diversity**} \\ \cline{2-9}  & **BLEU** & **Average** & **Greedy** & **Extreme** & **Emotion-a** & **Emotion-w** & **Distinct-1** & **Distinct-2** \\ \hline DESG-MLE & 1.61 & 0.550 & 0.376 & 0.379 & 0.728 & 0.468 & 0.0054 & 0.0671 \\ DESG-EV & 1.64 & 0.569 & 0.398 & 0.387 & 0.749 & 0.472 & 0.0056 & 0.0657 \\ DESG-BS & 1.73 & 0.624 & 0.439 & 0.489 & 0.776 & 0.659 & 0.0050 & 0.0464 \\ DESG-IM & 1.72 & 0.615 & 0.430 & 0.480 & 0.761 & 0.649 & 0.0059 & 0.0670 \\ \hline DESG & 1.74 & 0.633 & 0.450 & 0.434 & 0.813 & 0.690 & 0.0121 & 0.0869 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative results of ablation experiment. The emotional interaction pattern (EIP) is defined as \(P\big{(}e_{r}/e_{p}\big{)}=P\big{(}e_{r},e_{p}\big{)}/P\big{(}e_{p}\big{)}\), where \(e_{p}\) is the emotion category of the problem, and \(e_{r}\) is the emotion category of the output sequence. The DESG model EIP test results are shown in Figure 4.  
The experimental results shown in Figure 4 reveal that EIPs marked with darker shades appeared more frequently than those marked with lighter shades. From the above figure, we can observe some phenomena: (1) People tended to respond with the same emotional state as in the original post during communication (e.g., when a post expressed satisfaction, the response emotion was likely to be \"satisfaction\"). We see this is a common emotional interaction that indicate emotional empathy. (2) Emotions were diverse in the dialogue [33] (e.g., when an input sequence expressed happiness, the