## 5 Surrounding-Aware Individual Emotion Prediction Model
### Surrounding Attention Network
It is easily concluded that by using T-LSTM network to process irregular time series data and fusing time-evolving and surrounding influences, the SAEP model achieves the best prediction performance. Figure 10 suggests that the overall performances of the proposed complete model (SAEP) are better than that of the variants. Figure 9: Emotion prediction results “micro F1” on two datasets. (**a**) Twitter; (**b**) Microblog. ### Parameter Analysis

To achieve the optimal prediction performance of the SAEP model, this subsection explored how different hyperparameters affect the performance of the proposed model. These include cell size \(D\) and memory size \(Z\), which are hyperparameters related to model tuning. Extensive experiments were conducted by adjusting only the relevant hyperparameters to achieve the best performance while keeping the other hyperparameters unchanged. #### 6.8.1 Effect of Cell Size for Prediction Accuracy

The impact of \(D\) is explored, which is an important hyperparameter on the prediction performance of SAEP. Figure 11 shows the effect of \(D\) on model prediction performance on two datasets. In Figure 11, the prediction performance changes as the hyperparameter varies. It should be noted that in Figure 11, if the cell size \(D\) is too small or too large, the prediction micro F1 of SAEP tends to worsen. This indicates that selecting a proper cell size \(D\) is crucial for model performance. #### 6.8.2 Effect of Memory Size for Predict Accuracy

This subsection aims to evaluate the influence of memory size \(Z\). This study examines the trade-off between computational time and representational power. A large \(Z\) allows the model to calculate complex source representations, whereas \(Z=1\) limits the source representation to a single vector. The influence of \(Z\) on the prediction performance is shown in Figure 12. It can be seen that within a certain range, the performance of the proposed model improves. Figure 10: Ablation analysis results “micro F1” on two datasets. (**a**) Twitter; (**b**) Microblog. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Accuracy**} \\ \cline{3-8}  & & **Anger** & **Disgust** & **Fear** & **Happiness** & **Sadness** & **Surprise** \\ \hline \multirow{4}{*}{Twitter} & SAEP-s & 80.45 & 79.6 & 94.23 & 75.89 & 54.96 & 65.34 \\  & SAEP-s-t & 79.84 & 80.46 & 94.11 & 72.2 & 58.93 & 71.57 \\  & SAEP-TLSTM & 74.42 & 82.86 & 93.66 & 73.67 & 82.99 & 64.97 \\  & SAEP & **81.49** & **83.54** & **94.49** & **77.77** & **89.56** & **76.44** \\ \hline \multirow{4}{*}{Microblog} & SAEP-s & 74.85 & 69.66 & 88.43 & 81.58 & 83.82 & 65.22 \\  & SAEP-s-t & 83.2 & 68.26 & 85.62 & 69.16 & 85.46 & 61.15 \\ \cline{1-1}  & SAEP-TLSTM & 78.21 & 67.54 & 86.94 & 80.62 & 87.33 & 48.84 \\ \cline{1-1}  & SAEP & **91.31** & **83.48** & **91.81** & **89.23** & **90.29** & **87.35** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation analysis on two datasets. #### 6.8.3 Effect of Memory Size for Learning Procedure

Here, an experiment was conducted to observe the effect of varying values of the hyperparameter \(Z\) on the learning process. Figure 13a,b show the learning curves. It can be seen that the proposed model has a faster convergence while memory size \(Z\) increases on both datasets. Specifically, notice that \(Z=1\) fails to fit the data distribution. When the \(Z\) value increases, the SAEP model representation ability is enhanced, which significantly speeds up the inference process. ### Visualization

Finally, the emotion prediction results of different methods are visualized. Figure 14 shows the comparison of the seven methods on Microblog dataset. Figure 14a depicts the true emotions of users in Microblog social network in the future. Figure 14b represents the visualization result obtained by the proposed model. Figure 14c-g shows the

Figure 11: Model parameter analysis (cell size \(D\)). Figure 12: Model parameter analysis (Memory size \(Z\)).