## 5 Findings of the Study
### Extrinsic Evaluation
#### 5.3.2 Machine Learning Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{_myPersonality (Multiclass)_} \\ \hline
**Language Model** & **ML Classifier** & **Recall** & **Precision** & **F1** & **AUC** & **GM** & **Time Complexity** \\ \hline \multirow{6}{*}{Unigram} & SMO & 0.979 & 0.979 & 0.979 & 0.970 & 0.968 & 4.45 s \\ \cline{2-8}  & NB & 0.739 & 0.897 & 0.810 & 0.764 & 0.819 & 2.05 s \\ \cline{2-8}  & C4.5 & 0.967 & 0.965 & 0.966 & 0.960 & 0.965 & 92.54 s \\ \cline{2-8}  & KNN & 0.939 & 0.932 & 0.935 & 0.930 & 0.930 & 0.01 s \\ \cline{2-8}  & RF & 0.979 & 0.961 & 0.970 & 0.965 & 0.966 & 71.40 s \\ \cline{2-8}  & Ada & 0.968 & 0.962 & 0.965 & 0.954 & 0.964 & 9.91 s \\ \hline \multirow{6}{*}{Bigram} & SMO & 0.899 & 0.883 & 0.888 & 0.889 & 0.890 & 0.28 s \\ \cline{2-8}  & NB & 0.891 & 0.895 & 0.893 & 0.885 & 0.890 & 0.28 s \\ \cline{2-8}  & C4.5 & 0.888 & 0.887 & 0.857 & 0.884 & 0.886 & 0.53 s \\ \cline{2-8}  & KNN & 0.897 & 0.882 & 0.887 & 0.888 & 0.889 & 0.28 s \\ \cline{2-8}  & RF & 0.895 & 0.875 & 0.885 & 0.887 & 0.887 & 99.33 s \\ \cline{2-8}  & Ada & 0.893 & 0.895 & 0.894 & 0.888 & 0.892 & 0.24 s \\ \hline \multirow{6}{*}{Trigram} & SMO & 0.930 & 0.920 & 0.925 & 0.921 & 0.924 & 0.05 s \\ \cline{2-8}  & NB & 0.929 & 0.918 & 0.923 & 0.920 & 0.921 & 0.05 s \\ \cline{2-8}  & C4.5 & 0.918 & 0.914 & 0.916 & 0.911 & 0.915 & 0.05 s \\ \cline{2-8}  & KNN & 0.930 & 0.920 & 0.916 & 0.924 & 0.923 & 0.05 s \\ \cline{2-8}  & RF & 0.930 & 0.920 & 0.916 & 0.924 & 0.922 & 8.46 s \\ \cline{2-8}  & Ada & 0.920 & 0.920 & 0.920 & 0.916 & 0.919 & 0.04 s \\ \hline \multicolumn{8}{c}{_Sentiment140 (Multiclass)_} \\ \hline
**Language Model** & **ML Classifier** & **Recall** & **Precision** & **F1** & **AUC** & **GM** & **Time Complexity** \\ \hline \multirow{6}{*}{Unigram} & SMO & 0.995 & 0.995 & 0.995 & 0.989 & 0.989 & 172.43 s \\ \cline{2-8}  & NB & 0.841 & 0.939 & 0.887 & 0.814 & 0.863 & 24.68 s \\ \cline{2-8}  & C4.5 & 0.991 & 0.991 & 0.991 & 0.981 & 0.984 & 3379.83 s \\ \cline{2-8}  & KNN & 0.967 & 0.966 & 0.966 & 0.979 & 0.956 & 15.04 s \\ \cline{2-8}  & RF & 0.986 & 0.986 & 0.986 & 0.983 & 0.978 & 2589.97 s \\ \cline{2-8}  & Ada & 0.990 & 0.990 & 0.990 & 0.984 & 0.982 & 194.9 s \\ \hline \multirow{6}{*}{Bigram} & SMO & 0.959 & 0.958 & 0.958 & 0.954 & 0.987 & 654.59 s \\ \cline{2-8}  & NB & 0.284 & 0.815 & 0.421 & 0.376 & 0.264 & 64.33 s \\ \cline{2-8}  & C4.5 & 0.952 & 0.951 & 0.951 & 0.952 & 0.944 & 12,409.02 s \\ \cline{2-8}  & KNN & 0.954 & 0.946 & 0.946 & 0.953 & 0.941 & 27.45 s \\ \cline{2-8}  & RF & 0.948 & 0.946 & 0.947 & 0.948 & 0.940 & 5800.23 s \\ \cline{2-8}  & Ada & 0.954 & 0.951 & 0.952 & 0.944 & 0.949 & 157.20 s \\ \hline \multirow{6}{*}{Trigram} & SMO & 0.947 & 0.947 & 0.947 & 0.946 & 0.941 & 2.34 s \\ \cline{2-8}  & NB & 0.947 & 0.935 & 0.929 & 0.937 & 0.939 & 26.01 s \\ \cline{2-8}  & C4.5 & 0.947 & 0.947 & 0.947 & 0.939 & 0.944 & 690.05 s \\ \cline{2-8}  & KNN & 0.947 & 0.929 & 0.938 & 0.934 & 0.940 & 0.05 s \\ \cline{2-8}  & RF & 0.942 & 0.942 & 0.942 & 0.921 & 0.941 & 795.85 s \\ \cline{2-8}  & Ada & 0.945 & 0.945 & 0.945 & 0.922 & 0.942 & 126.08 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multiclass classification performance. In the comparison of tree-based classifiers, although the C4.5 and RF technically gave similar performances for both datasets, the time complexities to predict the training classes by the two classifiers were very complex for _Sentiment140_ as the dataset was comprised of high dimensional attributes. For instance, the use of unigram and bigram features from _Sentiment140_ caused the C4.5 model to take 3379.83 s and 12,409.02 s, respectively, to accomplish the prediction process. Regarding the processing time inefficiency, C4.5 built the network with 195 nodes and 98 leaves for the unigram feature, while it used 105 nodes with 53 leaves using bigram attributes. Meanwhile, for _myPersonality_, the model only generated a tree with 31 nodes and 16 leaves for unigram and 13 nodes with 7 leaves for bigram.