## 5 Surrounding-Aware Individual Emotion Prediction Model
### Surrounding Attention Network
Faced with massive posts, manual tagging is powerless. Instead, tags for automatic post labeling were used, which is a common method in previous studies [47; 48]. First, WordNet ([[http://wordnet.princeton.edu/](http://wordnet.princeton.edu/)]([http://wordnet.princeton.edu/](http://wordnet.princeton.edu/)), accessed on 1 May 2021) and HowNet ([[http://www.keenage.com/](http://www.keenage.com/)]([http://www.keenage.com/](http://www.keenage.com/)), accessed on 1 May 2021) dictionaries were used to obtain an average of more than 200 synonyms for each basic emotion category and manually verified them. For the Twitter dataset, a pre-trained RNN [18] was adopted to extract the emotion vectors from the user posts. For each post, the appearances of each emotion synonym category in the texts are computed. If the most frequent emotion corresponds to the emotion vector of the post, then the emotion vector is regarded as the ground truth. Otherwise, it was verified manually. Each post will be reviewed by three different volunteers to eliminate personal bias; then, all the volunteers are required to have a discussion to reach an agreed conclusion. The emotion distribution of this dataset is shown in Figure 7a. For the Microblog dataset, similar to the Twitter dataset, a pre-trained LSTM [49] was utilized to extract the emotion vectors from users' posts. The emotion distribution of this dataset is shown in Figure 7b. ### Baseline Methods

* Conditional Random Field (CRF) [47]: It is a graphical model based on conditional random field. The model predicts emotions by combining social correlations with emotional features. * LSTM [50]: An RNN model with a LSTM cell is trained to predict users' emotions, which impute the missing data with mean values in emotion time series. * GRU-D [51]: GRU-D is a novel deep learning model based on GRU. It takes two missing data patterns, masking and time intervals, to extract long-range temporal missing patterns in the time series and capture the informative missing data. GRU-D utilizes long-term temporal dependencies to achieve a better prediction performance. * Stacked Auto-Encoder (SAE) [52]: SAE is a famous deep learning model, which uses auto encoders as the basic components of deep network. It predicts emotions by learning temporal correlations in the emotion time series. * Temporal Convolutional Network (TCN) [53]: TCN is a forecasting framework based on convolutional neural network. TCN constructs stacked residual blocks based on dilated causal convolutional networks to capture the temporal dependencies of the emotion time series. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & \(|V|\) & \(|E|\) & \(PN\) & \(RN\) \\ \hline Twitter & 5136 & 27,142 & 451,029 & 705,018 \\ \hline Microblog & 5068 & 12,036 & 66,480 & 158,240 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Twitter and Microblog datasets. Figure 7: Percent of per emotion category on two datasets. (**a**) Twitter; (**b**) Microblog. ### Hyperparameter Settings

This section discusses the hyperparameter settings. The proposed model uses a 1-layer 32-unit T-LSTM encoder and a 1-layer 32 unit LSTM decoder. The memory size, \(Z\), is 32. The model is optimized using an Adam optimizer with a learning rate of \(1\times 10^{-3}\) in the encoder and \(5\times 10^{-3}\) in the decoder. The batch size was set to 256 and the dropout rate was set to 0.9. ### Evaluation Measures

Multiple criteria are used to evaluate SAEP model, including accuracy (_Acc_) and micro F1 (_Micro_F1_) [54], which are widely used for evaluating emotion prediction [12; 40; 55]. Accuracy describes how many of the predicted specific emotion examples were correct. Micro F1 can still make a reasonable evaluation of the classifier in the case of unbalanced samples, which is defined as follows.