# Machine Learning Approach for Personality Recognition in Spanish Texts
## 4 Results
Naive Bayes (DS5) and SMO (DS8) have good performance according to F-measure. The algorithms J48 and kNN have low performance with most datasets. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline DS4 & AdaBoost & 0.782258 & 0.795 & 0.782 & 0.786 & 0.939 \\ DS2 & Random Forest & 0.778226 & 0.792 & 0.778 & 0.782 & 0.933 \\ DS3 & Random Forest & 0.774194 & 0.783 & 0.774 & 0.777 & 0.937 \\ DS4 & Random Forest & 0.774194 & 0.783 & 0.774 & 0.777 & 0.937 \\ DS2 & AdaBoost & 0.770161 & 0.775 & 0.77 & 0.772 & 0.935 \\ DS3 & AdaBoost & 0.770161 & 0.776 & 0.77 & 0.772 & 0.932 \\ DS2 & Random Forest & 0.766129 & 0.777 & 0.766 & 0.769 & 0.938 \\ DS6 & AdaBoost & 0.762097 & 0.774 & 0.762 & 0.766 & 0.924 \\ DS7 & Random Forest & 0.758065 & 0.777 & 0.758 & 0.763 & 0.923 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top-ten classifiers according to F-measure. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline DS1 & AdaBoost & 0.782258 & 0.795 & 0.782 & 0.786 & 0.939 \\ DS3 & Random Forest & 0.778226 & 0.792 & 0.778 & 0.782 & 0.933 \\ DS4 & Random Forest & 0.774194 & 0.783 & 0.774 & 0.777 & 0.937 \\ DS2 & AdaBoost & 0.770161 & 0.775 & 0.77 & 0.772 & 0.935 \\ DS3 & AdaBoost & 0.770161 & 0.776 & 0.77 & 0.772 & 0.932 \\ DS6 & Random Forest & 0.75 & 0.767 & 0.75 & 0.755 & 0.929 \\ DS6 & AdaBoost & 0.762097 & 0.774 & 0.762 & 0.766 & 0.924 \\ DS7 & Random Forest & 0.758065 & 0.777 & 0.758 & 0.763 & 0.923 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Top-ten classifiers according to ROC area. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline DS1 & AdaBoost & 0.745968 & 0.774 & 0.746 & 0.754 & 0.911 \\ DS2 & Random Forest & 0.770161 & 0.775 & 0.77 & 0.772 & 0.935 \\ DS3 & Random Forest & 0.778226 & 0.792 & 0.778 & 0.782 & 0.933 \\ DS4 & AdaBoost & 0.782258 & 0.795 & 0.782 & 0.786 & 0.939 \\ DS5 & Naive Bayes & 0.629032 & 0.635 & 0.629 & 0.597 & 0.84 \\ DS6 & AdaBoost & 0.762097 & 0.774 & 0.762 & 0.766 & 0.924 \\ DS7 & Random Forest & 0.758065 & 0.777 & 0.758 & 0.763 & 0.923 \\ DS8 & SMO & 0.741935 & 0.741 & 0.742 & 0.74 & 0.858 \\ DS9 & AdaBoost & 0.729839 & 0.734 & 0.73 & 0.731 & 0.858 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best classifier for each dataset according to F-measure. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline DS1 & AdaBoost & 0.745968 & 0.774 & 0.746 & 0.754 & 0.911 \\ DS2 & Random Forest & 0.766129 & 0.777 & 0.766 & 0.769 & 0.938 \\ DS3 & Random Forest & 0.778226 & 0.792 & 0.778 & 0.782 & 0.933 \\ DS4 & AdaBoost & 0.782258 & 0.795 & 0.782 & 0.786 & 0.939 \\ DS5 & Naive Bayes & 0.629032 & 0.635 & 0.629 & 0.597 & 0.84 \\ DS6 & AdaBoost & 0.762097 & 0.774 & 0.762 & 0.766 & 0.924 \\ DS7 & Random Forest & 0.758065 & 0.777 & 0.758 & 0.763 & 0.923 \\ DS8 & Random Forest & 0.733871 & 0.747 & 0.734 & 0.738 & 0.923 \\ DS9 & Random Forest & 0.705645 & 0.715 & 0.706 & 0.709 & 0.921 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Best classifier for each dataset according to receiver operating characteristic (ROC) area. As can be observed in Tables 8-10, the best classifier is AdaBoost (F-Measure = 0.786 and ROC area = 0.939 for DS4 (276 features selected by Pearson correlation). Table 12 shows the measures for this classifier. The average ROC area of 0.939 indicates that the model separates the four classes very well. Table 12 also shows that measures for Steadiness are low. This phenomenon was observed for every classifier; therefore, this class is the hardest class to predict. DS4 was the dataset that provided the best performance to the classifiers.