## 5 Experimental Results and Analysis
### Analysis of Comparative Experimental Results  
Table 3 presents the final results of our model and all baseline models on the four datasets. By analyzing the final results, we can find that SETD-ERC reaches the state-of-the-art on the three datasets: IEMOCAP, MELD, and EmoryNLP.  
In our experiments, the benchmark model HiGRU, DialogueRNN, and COSMIC are based on recursive models. DAG-ERC, ERMC-DISGCN, and DialogueGCN use graph-based models. Through the experimental results, we can find that the classification results based on recursive models are relatively poor, indicating that the recursive model is not effective based on the characteristics of the coding context. The worst effect of the HiGRU is that it only focuses on encoding text information and ignores the importance of interactive information in the dialogue. DialogXL, DialogueCRN, and CoG-BART use different pre-training models. It can be found that the different pre-training models will affect the final classification effect, and DialogueCRN claims to be able to simulate human cognitive thinking, but it performs poorly on the short dialogue dataset and cannot cope with it. The theme of the dialogue is not continuous enough. Our model is always better than their models, which proves the superiority of our model.  
On DailyDialog, the improvement of our model on the classification results is not as significant as DAG-ERC. When comparing the dataset, we found that the reason for this result is that the dialogues in DailyDialog are relatively short, and its average number of dialogue turns is less than 8, but the advantage of our model is to handle multi-turn dialogue. If the number of dialogues is low, the advantages of the dialogue interaction module in our model cannot be fully performed. When our model is processing the IEMOCAP dataset with an average dialogue length of 49, our model's improvement of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **IEMOCAP** & **DailyDialog** & **MELD** & **EmoryNLP** \\ \cline{2-5}  & **Weighted-Avg-F1** & **Micro-F1** & **Weighted-Avg-F1** & **Weighted-Avg-F1** \\ \hline HiGRU (2019) & 59.79 & 52.01 & 56.92 & 31.88 \\ DialogueRNN (2019) & 62.75 & 50.65 & 57.03 & 31.27 \\ COSMIC (2020) & 63.05 & 56.16 & 64.28 & 37.10 \\ DAG-ERC (2021) & 68.03 & **59.33** & 63.65 & 39.02 \\ ERMC-DisGCN(2021) & 64.1 & * & 64.22 & 36.38 \\ DialogueGCN (2019) & 64.18 & * & 58.1 & 33.85 \\ DialogXL (2021) & 65.94 & 54.93 & 62.41 & 34.73 \\ DialogueCRN(2021) & 66.2 & * & 58.39 & * \\ CoG-BART (2022) & 66.18 & 56.29 & 64.81 & 39.04 \\ \hline Ours & **70.92** & 57.07 & **66.13** & **40.97** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance on the four datasets.  
classification results far exceeds other models, indicating that our model has the advantages of the emotional classification work in multi-party multi-turn dialogue. We effectively mine the original meaning and the derived meaning of the sentence through the syntactic structure and topic information and use the topic segmentation technology to the dialogue text to process the context information. The graph structure of SETD-ERC is specially designed for multi-party multi-turn dialogue to capture the speaker identities, enabling it to achieve better performance than other models.  
Overall, the performance of our model has met expectations. On the long-distance dialogue text dataset IEMOCAP, we surpass other models by 4% more, indicating that the topic segmentation technology solves the long-term dependency problem very well. Long texts are divided into topic-centered short text dialogues to reduce model calculation difficulty and increase classification accuracy. We use heterogeneous dialogue graphs to model dialogues, which well incorporates dialogue subject information, which is beneficial to classification results.