## 5 Surrounding-Aware Individual Emotion Prediction Model
### Surrounding Attention Network
\[Precision_{micro}=\frac{\sum_{d=1}^{|S|}TP_{d}}{\sum_{i=1}^{|S|}TP_{i}+\sum_{d=1 }^{|S|}FP_{d}} \tag{25}\]

\[Recall_{micro}=\frac{\sum_{d=1}^{|S|}TP_{d}}{\sum_{d=1}^{|S|}TP_{d}+\sum_{d=1 }^{|S|}FN_{d}} \tag{26}\]

\[Micro\_F1=2\cdot\frac{Precision_{micro}\cdot Recall_{micro}}{Precision_{ micro}+Recall_{micro}} \tag{27}\]

where \(Precision_{micro}\) is micro precision, and \(Recall_{micro}\) is micro recall. \(TP_{d}\) is the number of emotions correctly predicted as \(d\)-th emotion. \(FP_{d}\) indicates the number of emotions not labeled as \(d\)-th emotion but predicted as \(d\)-th emotion. \(FN_{d}\) represents the number of emotions incorrectly predicted as \(d\)-th emotion. \(|S|\) is the dimension of the emotion vector. ### Performance Comparison

To illustrate the effectiveness of the SAEP model, the performance of the proposed model is compared with that of serval other models, presented in Section 6.3. Table 2 and Figure 8 show the comparative results of different approaches when predicting emotions, and the overall performances of the different approaches on the two datasets are shown in Figure 9. The results for the two datasets show that SAEP model is more competitive. Table 2 and Figure 9 demonstrate that, although CRF considers some correlation features (the influence between users and neighbors), this method failed to extract the underlying structure in irregular time series data. LSTM can capture the temporal relationships of users' emotional changes, but LSTM fails to handle irregular time series data, and replacing missing data with average values harms prediction performance. GRU-D designs a recurrent architecture that uses time intervals and masking information to update the missing data. However, it ignores the importance of global temporal background contextual information. SAE is a good approach for predicting emotion, which considers the temporal correlations of emotion changes inherently, but it lacks the modeling of neighbors' influence. TCN is a kind of convolutional network for time series data, which is with less computation consuming, better ability in capturing individual emotion time-evolving regularities, and higher adaptability for non-linear data. However, it also lacks the modeling of surrounding-influence in social networks. The best comprehensive performance of the SAEP model on two datasets indicates that combining social and temporal contextual information is helpful for predicting users' future emotions. As shown in Figure 9, the proposed model significantly outperforms all the baseline methods by achieving the highest micro F1 on the two datasets. The SAEP model improves by 4.21-14.84% and 7.30-13.41% on Twitter and Microblog in terms of micro F1, respectively. The first reason for the better performance of the SAEP model is that the T-LSTM network is employed as the encoder to process irregular time series data, which can better capture thetime dependence of emotional states. Second, the proposed model considers the influence of neighbors' on users' emotions, which can assist in exploring the changes in emotions between users' interactions.