# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 5 Experiment
### Experimental Results Analysis  
(1) Performance comparison with existing methods:  
The results of the myPersonality and essay dataset classification are summarized in Tables 4-7. We report the accuracy and F1-score of personality recognition. Note that we report only the best results from existing models [16; 17; 26; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. The results in our model used the parameters that achieved the best results in the dataset. The results clearly showed that personality GCN outperformed the other methods in supervised personality recognition settings. Tables 4 and 5 provide the accuracy and F1-score in the myPersonality dataset comparison between the existing model and our methods. From Table 4, we can see that the personality GCN model was 0.49% lower in the personality trait of neuroticism (NEU) and was higher than the existing model in the remaining four personality traits and the overall average. As seen in Table 5, although our model was 0.07 and 0.04 lower in agreeableness (AGR) and openness (OPN) traits, respectively, our average level was higher than the existing model of 3.6%. The highest accuracy and F1-score obtained from personality GCN were 80% and 0.85, respectively. The highest average accuracy and F1-score were 76.6% and 0.768, respectively, also from personality GCN. in the two datasets and could effectively improve the accuracy and F1-score compared with other methods [16; 17; 26; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. As can be seen from Tables 4-7, the three personality traits OPN, extroversion (EXT), and NEU were very closely related to the text information, and the features of OPN, EXT, and NEU could be well extracted from the text information. From another perspective, the feature information of personality traits AGR and conscientiousness (CON) in text information was not obvious. The experimental results of the two datasets showed that the personality GCN showed better performance in the short text dataset. We guessed that short text could be better learned than long text, and for long text, it was easy to lose information during the learning process. (2) Performance with different personality GCN layers:  
Figures 4 and 5 show that the number of personality GCN layers played an important role in the process of information propagation. In the one-layer personality GCN, the information only flowed between neighbors, so for our user-document-word tripartite graph, the number of personality GCN layers was at least three, so that the information could flow from the user to the word node. To better understand this, we kept the parameters in the experiment consistent and drew Figures 4 and 5 to display the relation between the personality GCN layer and the performance (accuracy and F1-score) of our model. \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 Word n-grams & 51.72\% & 50.26\% & 53.10\% & 50.79\% & 51.52\% & 51.48\% \\ Mairesse & 55.13\% & 58.09\% & 55.35\% & 55.28\% & 59.57\% & 56.68\% \\ IG & 54.74\% & 57.54\% & 57.54\% & 55.55\% & 61.83\% & 57.44\% \\ PCA & 55.75\% & 58.31\% & 56.71\% & 57.30\% & 59.38\% & 57.49\% \\ CNN + Mairesse & 58.09\% & 59.38\% & 56.71\% & 57.30\% & 59.38\% & 58.17\% \\ Personality GCN & **60.00\%** & 63.00\% & **57.70\%** & **59.10\%** & **64.80\%** & **60.92\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Personality GCN classification results of accuracy on the essay dataset.