## 5 Surrounding-Aware Individual Emotion Prediction Model
### Surrounding Attention Network
Figure 13: Comparison concerning varying memory size \(Z\) for the training loss curve on two datasets. (**a**) Twitter; (**b**) Microblog. results of the other five approaches. In terms of the micro F1 of emotion prediction, SAEP, CRF, LSTM, GRU-D, SAE, TCN reached 63.33%, 52.75%, 49.92%, 50.74%, 54.50%, and 56.03%, respectively. Figure 14b intuitively illustrates that the emotions predicted by the SAEP model are more in line with the real situation. Differences occur in the prediction performance between these baseline methods and the proposed model, and these differences can be explained from two aspects. First, SAEP not only considers the changing trend of the user's emotions, but also considers the influence of the emotions of the user's neighbors on the user. Second, the proposed model introduces a T-LSTM network to handle irregular emotion time series, which can effectively extract the underlying structure in the irregular emotion time series. In addition, Figure 14a indicates that neighboring users have a greater probability of similar emotions. This can be explained by the emotional influence of the neighbors. Figure 14: Visualization of emotion prediction with different methods on Microblog (**a**) Original (**b**) SAEP (**c**) CRF (**d**) LSTM (**e**) GRU-D (**f**) SAE (**g**) TCN. ## 7 Conclusions

The widespread use of social media has facilitated people's communication as well as the emotion propagation in social networks greatly, and is therefore bringing the enormous impacts on social psychological cognition and group behaviors than ever before. This issue of emotion prediction on social media has become a new concerning hot point in social media analytics. This paper aims to predict individuals' future emotions and a surrounding-aware individual emotion prediction model-based deep encoder-decoder architecture is proposed for individual future emotion prediction by considering the influence of time-evolving and individual's neighbors. Based on individual emotion time series, the time-evolving attention network extracts the memory representation of an individual to obtain the time-evolving context to better capture the individual emotional change rule and emotional preference. The surrounding attention network obtains the surrounding context from the set of neighbors' emotion time series to better learn the influence of neighbors' emotions. Experiments on two datasets (from two social networks) demonstrate that the SAEP model achieves good results and outperforms several baseline methods for emotion prediction. In addition, this paper validated the effectiveness of the proposed time-evolving influence and surrounding influence by means of ablation experiments, which proves the importance of surrounding influence and time-evolving influence in predicting individuals' future emotions. Furthermore, research findings in ablation experiments indicated that T-LSTM network as the basic component of SAEP model to encode an irregular time series have significant impacts on the model performance. Moreover, to explore the influence of different hyperparameters on the model performance and minimize system load, parameter analysis experiments are conducted. The emotion prediction results of different methods on Microblog are visualized to intuitively understand the effectiveness of the SAEP model. In the future, more effective patterns in social networks to predict individual emotions will be explored, such as changes in user relationships and user interactions. Simultaneously, a new neural network technology will be used to enhance performance. Furthermore, whether the method can be applied in other heterogeneous networks needs to be further explored. Y.W. : Conceptualization, Methodology, Software, Writing-original draft, Visualization, Investigation. Y.D. : Conceptualization, Supervision, Project administration, Funding acquisition. J.H. : Validation, Formal analysis. X.L. : Conceptualization, Project administration. X.C. : Data curation, Resources.