## 5 Surrounding-Aware Individual Emotion Prediction Model
### Surrounding Attention Network
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Accuracy**} \\ \cline{3-8}  & & **Anger** & **Disgust** & **Fear** & **Happiness** & **Sadness** & **Surprise** \\ \hline \multirow{8}{*}{Twitter} & CRF & 79.52 & 81.25 & 88.42 & 72.41 & 78.28 & 71.63 \\  & LSTM & 80.31 & 87.75 & **94.72** & 74.65 & 89.17 & 27.60 \\  & GRU-D & 75.92 & **88.90** & 93.28 & 67.52 & 89.33 & 74.35 \\  & SAE & 72.09 & 88.58 & 92.96 & 55.42 & 88.67 & 67.10 \\  & TCN & 78.76 & 81.42 & 89.09 & 72.69 & 85.14 & 66.00 \\  & SAEP & **81.49** & 83.54 & 94.49 & 77.77 & **89.56** & **76.44** \\ \hline \multirow{8}{*}{Microblog} & CRF & 80.33 & 81.94 & 79.36 & 71.52 & 82.27 & 69.38 \\  & LSTM & 77.59 & 87.03 & 79.68 & 67.96 & 82.78 & 58.83 \\ \cline{1-1}  & GRU-D & 76.87 & 84.87 & 82.02 & 67.78 & 85.00 & 57.92 \\ \cline{1-1}  & SAE & 77.47 & **87.47** & 81.76 & 67.78 & 84.62 & 58.19 \\ \cline{1-1}  & TCN & 80.91 & 81.74 & 80.11 & 76.32 & 87.10 & 64.31 \\ \cline{1-1}  & SAEP & **91.31** & 83.48 & **91.81** & **89.23** & **90.29** & **87.35** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of emotion prediction of SAEP with five baseline approaches on two datasets. Figure 8: Comparison of the accuracy of different emotion categories with different methods (**a**) Anger (**b**) Disgust (**c**) Fear (**d**) Happiness (**e**) Sadness (**f**) Surprise. ### Ablation Analysis

This subsection aims to illustrate the effectiveness of different parts of the proposed model. The SAEP model considers multiple factors that influence users' emotions in social networks: time-evolving influence and surrounding influence. Moreover, the T-LSTM network is introduced as the encoder of the proposed model to handle irregular emotion time series. Here, this subsection investigated the time-evolving influence, surrounding influence, and T-LSTM encoder to help improve the prediction performance. The contributions of each key component are tested by removing it from the proposed model and comparing their performances. Some variants of the proposed model are listed:

* SAEP: This is the proposed complete model. The parameter settings for this model are described in Section 6.4. * SAEP-s: This model removes the surrounding attention network. * SAEP-s-t: This variant removes both surrounding and time-evolving attention networks in the proposed model, meaning it is an RNN with a LSTM cell. * SAEP-TLSTM: This model employs the standard LSTM as the encoder instead of T-LSTM. The remaining settings for this model are the same as for SAEP model. The experimental results for the different variants are summarized in Table 3. Figure 10 further compares the experimental results with the corresponding micro F1. Table 3 illustrates that for most emotion categories, the prediction performance will be lower when each of the three key components is removed. Previous studies have revealed that social information plays a crucial role in image-based emotion prediction tasks [47; 48]. The experimental results further verify that the surrounding influence can also help improve the performance of text-based tasks, especially on the Microblog dataset. Table 3 indicates that the performance of SAEP model is better than that of SAEP-s on two datasets, which indicates that the surrounding attention network can effectively model the surrounding influence. SAEP-s outperforms SAEP-s-t model in terms of micro F1 on the two datasets. This is because the time-evolving attention network can capture the complex patterns of emotions that evolve over time. In addition, the performance of SAEP-TLSTM is not as good as that of the proposed complete model SAEP, which illustrates the decrease in the micro F1 when using the standard LSTM network as the encoder of the SAEP model. Evidently, T-LSTM as the encoder can effectively process irregular time series data, and in this way, the model performance is improved significantly.