# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 5 Experiment
### Experimental Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Traits**} \\ \cline{2-7}  & **EXT** & **NEU** & **AGR** & **CON** & **OPN** & **Average** \\ \hline Machine learning & 0.60 & 0.570 & 0.580 & 0.560 & 0.630 & 0.588 \\ IG & 0.541 & 0.574 & 0.557 & 0.55 & 0.618 & 0.568 \\ PCA & 0.556 & 0.583 & 0.563 & 0.560 & 0.619 & 0.576 \\ PSO & 0.620 & 0.630 & 0.620 & 0.650 & 0.700 & 0.644 \\ Personality GCN & **0.670** & **0.690** & **0.690** & **0.680** & 0.670 & **0.680** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Personality GCN classification results of F1-score on the essay dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Traits**} \\ \cline{2-7}  & **EXT** & **NEU** & **AGR** & **CON** & **OPN** & **Average** \\ \hline Machine learning & 0.65 & 0.53 & 0.50 & 0.55 & 0.62 & 0.57 \\ Pearson’s r & 0.69 & 0.61 & 0.63 & 0.64 & 0.66 & 0.646 \\ PSO & 0.77 & 0.71 & 0.77 & 0.69 & 0.79 & 0.746 \\ SMOTETomek & 0.73 & 0.73 & 0.75 & 0.63 & 0.82 & 0.732 \\ Personality GCN & **0.85** & **0.79** & 0.70 & **0.75** & 0.78 & **0.768** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Personality GCN classification results of F1-score on the myPersonality dataset. We report the accuracy and F1-score in two datasets under different GCN layers \(L\) among [1,2,3,4,5]. It is worth noting that we only drew the line when \(L\leq 5\) because there was no improvement when \(L>5\). From Figures 4 and 5, we can see that the one-layer GCN had a relatively poor performance on both datasets, while when the number of layers was three or four, both datasets achieved the best performance, which illustrated that the one-layer GCN could not transfer information from the user node to the word node and that at least three layers of the GCN could capture the word co-occurrence between the user node and the word node. When \(L>4\), the accuracy and F1-score declined to different degrees between the two datasets, and we assumed that the word representation was distorted due to too many instances of information propagation. (3) Discussion of personality GCN:  
From the experimental results, it could be seen that the proposed personality GCN could improve personality recognition results. There were two main reasons why personality GCN performed well. (1) The personality graph could capture user-document relationships, document-word relationships, and word-word relationships and use information transfer and word co-occurrence to achieve global information sharing. (2) When identifying user personality, we considered that there was a certain correlation between the five personality traits of a person. Instead of using five separate sets of  
Figure 4: Accuracy and F1-score in the myPersonality dataset with different numbers of personality GCN layers. Figure 5: Accuracy and F1-score in the essay dataset with different numbers of personality GCN layers. feature information, we shared a set of information functions to classify the five personality traits. Considering the correlation between the five personality traits also helped to improve the accuracy of personality recognition.