## 4 Experiments
### Results and Analysis  
We compare the performance of our model with baselines on the JDDC dataset. The experimental results are shown in Table 3.  
Firstly, by comparing the BiGRU, BERT, and ERNIE methods to treat each utterance of the conversation as a single-sentence instance experiment, it is found that the pre-trained model has an advantage in the semantic encoding of text compared with the BiGRU model. The F1 values of the BiGRU model are 2.08 and 2.24 lower than the values of BERT and ERNIE, respectively. At the same time, the semantic coding effect of the ERNIE pre-training model is slightly better than the BERT pre-training model, which is consistent with the experimental results provided by Baidu. In addition, The ERNIE model without considering context has a lower F1 value than that of the c-LSTM, BiGRU-Att, and CMN models considering context, which indicates that the context element of dialogue plays a vital role in sentiment recognition in dialogue sentiment analysis. Secondly, the experimental results of DiaogueRNN, DiaogueGCN, and our models are significantly better than those of c-LSTM, BiGRU-Att, and CMN models by comparing c-LSTM, BiGRU-Att, CMN, DiaogueRNN, DiaogueGCN, and our model. The reason is that compared with other models, DiaogueRNN, DiaogueGCN, and our model all learn the affective interaction relationship between speakers. Although all three models incorporate the affective interaction dependence between speakers, DiaogueGCN and our model use the graph convolutional neural network to fuse the utterance neighborhood information to obtain the text representation of the sentence, so the experimental results of DiaogueGCN and our model are better than the DiaogueRNN model. Finally, the macro-F1 value of our model is 1.16 higher than that of DiaogueGCN model. Compared with the DiaogueGCN model, our model uses ERNIE pre-trained model for the dialogue text embedding and adopts multiple GRU and attention mechanisms to extract the semantic features of context more comprehensively to obtain a better sentiment classification performance. .3.1 Effectiveness Analysis of Contextual Semantic Information and Affective Interaction Information  
We conduct combined comparative experiments from the aspects of the contextual semantic encoder and speaker affective interaction encoder to study the influence of the different components of our model on the emotion recognition results, the specific hybrid model as shown in Table 4, and the experimental results of different models as shown in Table 5. In Table 5, Our model(a) is composed of a conversation text encoder; Our model(b) is composed of a conversation text encoder and an affective interaction encoder; and Our model(c) is composed of a conversation text encoder and a context semantic encoder. Our model is thus composed of a conversation text encoder, a context semantic encoder, and an affective interaction encoder. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**F1 Value**} & \multirow{2}{*}{**Macro-F1**} \\ \cline{2-2} \cline{4-7}  & **Very Negative** & **Negative** & **Neutral** & **Positive** & **Very Positive** \\ \hline BiGRU & 50.5 & 55.1 & 78.1 & 72.3 & 63.3 & 63.86 \\ BERT & 52.1 & 56.6 & 82.2 & 74.2 & 64.6 & 65.94 \\ ERNIE & 52.3 & 56.6 & 82.5 & 74.3 & 64.8 & 66.1 \\ c-LSTM & 55.2 & 59.7 & 82.2 & 76.1 & 67.2 & 68.08 \\ BiGRU-Att & 55.7 & 60.1 & 82.7 & 76.5 & 67.8 & 68.56 \\ CMN & 56.9 & 60.9 & 82.8 & 76.7 & 69.1 & 69.28 \\ DiaogueRNN & 58.5 & 61.9 & 83.3 & 77.5 & 70.4 & 70.32 \\ DiaogueGCN & 59.2 & 62.5 & **83.3** & 77.6 & 71.1 & 70.74 \\ Our model & **60.3** & **64.6** & 83.2 & **78.9** & **72.5** & **71.90** \\ \hline \end{tabular}
\end{table} 
Table 3: The performances of different models. According to the experimental results of Tables 4 and 5, the F1 value of affective tendency classification has been gradually improved with the addition of a contextual semantic encoder and speaker affective interaction encoder.