# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 4 Results and Discussion  
This paper used correlation analysis to select features that are closely related to mood states in order to establish the feature set. Through the feature extraction, 25 significant features were obtained, including 5 features from the first 3 categories and 20 sentiment-related features from the fourth category. Further, the non-sentimental features contained registration time, the number of users that one follows, the average number of comments, the number of tweets that are reposted by Weibo users, and the average comments of blogs that are reposted by Weibo users. The mood states feature comprises different textual sentiment-related features within 1 week, 1 month, 3 months, 6 months, and 1 year. This indicates that user's mood state is related to both the text expression and stable statistical characteristics. This endorses the psychological conclusion that the user's mood state is a kind of psychological characteristic that has a correlation between the personality and the affect [42,43], considering that studies have shown that the personality could be automatically acquired through users' social network behavior [28]. The result also indicates that up to 80% of the features are emotional features, demonstrating that text expression plays an important role in calculating users' mood state. Table 4 displays the correlation coefficients between the extracted features and the different dimension of mood states, including the total score of POMS. In addition, 1 week, 1 month, 3 months, 6 months, and 1 year represent the average correlation coefficient of the sentimental score of the text during these periods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Features** & **Tension** & **Anger** & **Fatigue** & **Depression** & **Vigor** & **Confusion** & **Esteem** & **Total Score** \\ \hline Registration date & \(-0.145\) & \(-0.084\) & \(-0.22\)**\({}^{**}\)** & \(-0.089\) & \(0.07\) & \(-0.074\) & \(-0.068\) & \(-0.144\) \\ \hline Number of follows & \(0.124\) & \(0.135\) & \(0.118\) & \(0.041\) & \(-0.01\) & \(0.119\) & \(0.04\) & \(0.169\)\({}^{*}\) \\ \hline Average comments & \(-0.016\) & \(-0.166\)\({}^{*}\) & \(0.131\) & \(-0.018\) & \({}^{-0.197}_{**}\) & \(0.025\) & \(-0.14\) & \(-0.081\) \\ \hline Average reposts of & \(0.349\)\({}^{***}\) & \(0.191\)\({}^{*}\) & \(0.085\) & \(0.175\)\({}^{*}\) & \(-0.055\) & \(0.223\)\({}^{**}\) & \(-0.006\) & \(0.234\)\({}^{**}\) \\ \hline Average comments & \(-0.079\) & \(-0.087\) & \(-0.187\)\({}^{*}\  
These results show that the average repost number of blogs reposted by Weibo users has significant positive correlation with several mood state dimensions, including tension, anger, depression, confusion and total score, indicating an important non-sentimental feature. The dimension of fatigue and depression are both negatively correlated with several sentimental score of tweets in different time span while other dimensions do not have the same level of correlations. Figure 2 shows the correlation trend diagram regarding the mood states of tweets within each time span. The correlation coefficient trend of sentimental features within 1 year is obviously different from other time spans. Perhaps this is just because 1 year is too long for users' mood state to be stable. The sentimental features of tweets within 1 year have a much higher correlation with the dimensions of tension and anger. While considering the dimensions of vigor and esteem, the sentimental features of tweets have a higher correlation within a much shorter time span. This study employed the 10-fold cross-validation to train and validate the model, and the root-mean-square error (RMSE) was used as the evaluation index to evaluate the model. The RMSE is the most common evaluation index in the machine learning regression model, which is also called root-mean-square deviation (RMSD).