# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 5 Conclusions  
This study proposed cyber psychometrics method to calculate mood states of social network users automatically, adopting natural language processing and the machine learning method. Some conclusions can be drawn through analysis and discussion.  
Through the correlation analysis of users' POMS scores and their sentiment expressed using Weibo text, this study found that the user's mood state is not only related to the text expression,  
Figure 3: The root-mean-square error (RMSE) of different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mood State** & **Linear Regression** & **Lasso Regression** & **Gaussian Regression** & **Multitask Regression** \\ \hline \hline Tension & 0.784 & 0.434 & 0.978 & 0.324 \\ Anger & 0.736 & 0.391 & 1.661 & 0.353 \\ Fatigue & 0.411 & 0.331 & 1.219 & 0.319 \\ Depression & 0.909 & 0.442 & 1.380 & 0.355 \\ Vigor & 0.487 & 0.258 & 0.713 & 0.243 \\ Confusion & 0.600 & 0.365 & 0.908 & 0.315 \\ Esteem & 0.289 & 0.214 & 0.790 & 0.210 \\ Total score & 0.583 & 0.503 & 1.297 & 0.312 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The error ratio of different models.  
but also to stable statistical characteristics. Users' emotional expressions on the micro-blog text played an important part in the process of obtaining their true mood states. The correlation analysis results also showed that sentiment-related feature sets of different time span have different correlation characteristics with users' mood states, and the emotional features for \(1\) year evidently differ from those of other timespans.  
Compared with other classic single-task machine learning methods, the cyber psychometrics method adopting multi-task regression performed better. The experiment compared the multi-task learning method and the classic single-task regression algorithms, and it was found that the multi-task machine learning method can enhance the performance in RMSE and error ratio by learning the correlation among different dimensions of mood states.  
This method could be applied in psychological measurement and the prediction of the behavior of social network users. Such an automatic calculation method may be widely applied in predicting psychological variables and other characteristics of users considering the rapid development of internet, especially the social network. However, there were still some limitations in that the experimental subjects were college students of similar ages, and the characteristics of mood states concerning those with different identities and ages were not considered. In addition, this paper constructed a mood state lexicon (about 3000 words) to extract the users' sequential sentiment-related features. The scale is relatively small and the feature extraction process for the mood states of users under large-scale data requires further research.  
This study is supported by Program of National Natural Science Fund \"Service selection optimization of SaaS based on user preference-aware in cloud computing\" (No. 71271099) and the National Natural Science Fund \"User preference extraction and interactive personalized recommendation: research based on the screen hot spot\" (No. 71571084).  
The study was carried out in collaboration between all authors. [PERSON] and [PERSON] designed the research topic. [PERSON] and [PERSON] conducted the experiment and wrote the paper. [PERSON], [PERSON] and [PERSON] examined the experimental data and checked the experimental results. All authors agreed to submission of the manuscript.  
The authors declare no conflict of interest.