## 5 Results
### Model Selection
The best performing solution for the _Agreeableness_ trait was when using the SVR model that included a subset of LIWC dimensions correlating with the \(T\)-\(F\) MBTI type (e.g., _you_, _article_ and _sad_) in the feature set. While not the best performing model, SVR still managed to outperform the baseline approach using several different feature sets, especially when predicting the _Agreeableness_ trait, for which it outperformed the baseline on every single experiment conducted. We propose that these results are largely due to the SVR's nature to work well with smaller sets of data, as well as the error function on which it relies. However, we must remark that SVR had worse results than the baseline on both the _Openness_ and _Extroversion_ traits--both of which have shown the overall highest correlations with \(n\)-gram features, indicating that SVR places less importance on this particular set of features when making predictions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Features** & **Openness** & **Consicent.** & **Extroversion** & **Agreeable.** & **Neuroticism** \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.256 & 0.270 & 0.407 & 0.263 & 0.296 \\  & **S-N** & 0.265 & 0.275 & 0.392 & 0.273 & 0.290 \\  & **T-F** & 0.250 & 0.272 & 0.381 & 0.289 & 0.283 \\  & **J-P** & 0.246 & 0.283 & 0.384 & 0.269 & 0.272 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.167 & 0.266 & 0.358 & 0.264 & 0.281 \\  & **S-N** & 0.181 & 0.268 & 0.347 & 0.268 & 0.270 \\  & **T-F** & 0.170 & 0.267 & 0.320 & 0.256 & 0.247 \\  & **J-P** & 0.168 & 0.270 & 0.327 & 0.263 & 0.259 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.269 & 0.270 & **0.408** & 0.264 & 0.310 \\  & **S-N** & **0.283** & 0.283 & 0.397 & 0.274 & 0.298 \\  & **T-F** & 0.263 & 0.272 & 0.388 & 0.289 & 0.296 \\  & **J-P** & 0.267 & **0.285** & 0.391 & 0.265 & 0.292 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.255 & 0.269 & 0.396 & 0.260 & **0.312** \\  & **S-N** & 0.263 & 0.276 & 0.384 & 0.272 & 0.288 \\  & **T-F** & 0.245 & 0.272 & 0.375 & 0.284 & 0.274 \\  & **J-P** & 0.254 & **0.285** & 0.378 & 0.268 & 0.266 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.230 & 0.274 & 0.370 & 0.282 & 0.291 \\  & **S-N** & 0.232 & 0.267 & 0.361 & 0.289 & 0.286 \\  & **T-F** & 0.242 & 0.274 & 0.359 & **0.298** & 0.279 \\  & **J-P** & 0.242 & 0.282 & 0.358 & 0.294 & 0.279 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.235 & 0.179 & 0.368 & 0.223 & 0.228 \\  & **S-N** & 0.234 & 0.181 & 0.369 & 0.220 & 0.231 \\  & **T-F** & 0.239 & 0.178 & 0.359 & 0.231 & 0.230 \\  & **J-P** & 0.249 & 0.171 & 0.359 & 0.210 & 0.227 \\ \hline \multirow{5}{*}{**S-N**} & **E-I** & 0.224 & 0.219 & 0.337 & 0.249 & 0.285 \\  & **S-N** & 0.216 & 0.219 & 0.349 & 0.253 & 0.284 \\ \cline{1-1}  & **T-F** & 0.222 & 0.224 & 0.335 & 0.250 & 0.287 \\ \cline{1-1}  & **J-P** & 0.221 & 0.217 & 0.337 & 0.256 & 0.286 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Scores for predicting the gold-standard Big Five labels using combinations of \(n\)-grams, MBTI/Enneagram predictions and LIWC dimensions selected through process outlined in the previous subsection (Table 14) evaluated on different models. Another well-performing model is the Huber regressor, which demonstrated overall exemplary performances when predicting the _Conscientiousness_ and _Neuroticism_ personality traits, with the results for the other three traits also showing promise. When it comes to predictions for the _Neuroticism_ trait, we suggest that good results are due to the Huber regressors capability of working well with outliers, possibly being more capable of working with the _Neuroticism_ trait, which demonstrated the presence of bimodal distribution. Out of all the models, the overall best performing one seems to be Elastic-Net, which performed best when predicting three of the five Big Five traits, namely _Openness_, _Conscientiousness_ and _Extroversion_.