## 5 Results
### Feature Analysis
#### 5.1.3 Language-Based Features  
When conducting analysis into psycholinguistic features, it should be noted that several researchers in the past have found correlations between the Big Five traits and different LIWC dimensions [49; 59; 104]. However, as the exact list of LIWC dimensions that correlate with each Big Five trait tends to differ on a dataset basis, it is possible that contextual information, in addition to personality, can have a huge influence on language usage that is measured by LIWC. While it is possible to perform detailed research into how the Big Five personality traits have influenced language usage on the social media platform Reddit, such a study and its results could potentially introduce information leak into the prediction model if used for the regression task. In addition to this, the usage of psycholinguistic information based on its relationship with the Big Five personality traits would possibly minimize the effectiveness of MBTI/Enneagram predictions present in the feature set. Instead, we focused on a statistical analysis of the MBTI types and how they influence language use on Reddit, as suggested by data present in the PANDORA dataset. Through this approach, we not only open the possibility of this information being leveraged in our prediction model but also provide insight into the linguistic nature of MBTI types. Tables 10 and 11 include information about all the correlations present between LIWC dimensions and MBTI types. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Introverted** & **Intuitive** & **Thinking** & **Perceiving** \\ \hline LIWC Dim. & & & & \\  achieve & \(-0.020\) & \(0.050\)\({}^{**}\) & \(0.091\)\({}^{**}\) & \(-0.057\)\({}^{**}\) \\  adverb & \(-0.010\) & \(-0.012\) & \(-0.193\)\({}^{**}\) & \(0.030\)\({}^{**}\) \\  affect & \(-0.079\)\({}^{**}\) & \(0.014\) & \(-0.208\)\({}^{**}\) & \(0.025\)\({}^{*}\) \\  AllPunc & \(0.024\)\({}^{*}\) & \(-0.001\) & \(0.013\) & \(-0.009\) \\  anger & \(-0.034\)\({}^{**}\) & \(0.002\) & \(0.068\)\({}^{**}\) & \(0.075\)\({}^{**}\) \\  anx & \(-0.027\)\({}^{**}\) & \(0.009\) & \(-0.184\)\({}^{**}\) & \(-0.038\)\({}^{**}\) \\  Apostro & \(-0.019\) & \(-0.022\)\({}^{*}\) & \(-0.100\)\({}^{**}\) & \(0.010\) \\  article & \(0.049\)\({}^{**}\) & \(0.034\)\({}^{**}\) & \(0.197\)\({}^{**}\) & \(0.014\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Correlation between MBTI types and LIWC features present in the PANDORA dataset. LIWC features that correlate with at least one MBTI type are shown in the table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Openness** & **Consequent.** & **Extroversion** & **Agreeableness** & **Neuroticism** \\ \hline \hline \multicolumn{1}{c}{\(n\)-grams +} & & & & & \\  Quartile & 0.243 & 0.222 & 0.372 & 0.266 & 0.246 \\  Preds. & & & & & \\ \(n\)-grams + & & & & & \\  Quartile + & **0.259** & 0.258 & 0.386 & 0.249 & 0.265 \\  Other Preds. & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pearson correlation coefficient between the actual Big Five traits and predictions achieved through the usage of \(n\)-grams, MBTI/Enneagram predictions and quartile-split Big Five class predictions as features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Openness** & **Consequent.** & **Extroversion** & **Agreeableness** & **Neuroticism** \\ \hline \hline \multicolumn{1}{c}{Quartile Preds.} & & & & \\  Openness & \(-0.025\) & \(-0.025\) & \(-0.160\) & \(-0.011\) & \(-0.018\) \\  Conscient. & \(-0.071\) & \(-0.251\) & \(-0.010\) & \(-0.039\) & \(-0.078\) \\  Extroversion & \(-0.171\) & \(-0.066\) & \(-0.350\) & \(-0.039\) & \(-0.011\) \\  Agreeableness & \(-0.011\) & \(-0.086\) & \(-0.028\) & \(-0.284\) & \(-0.169\) \\  Neuroticism & \(-0.039\) & \(-0.107\) & \(-0.067\) & \(-0.152\) & \(-0.234\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Correlations between the gold-standard Big Five labels and predictions that use the quartile-delimited Big Five categories as features.