## 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{6}{c}{_myPersonality (One vs. All)_} \\ \hline
**Language Model** & **ML Classifier** & **Recall** & **Precision** & **F1** & **AUC** & **GM** & **Time Complexity** \\ \hline \multirow{6}{*}{Unigram} & SMO & 0.999 & 0.999 & 0.995 & 0.997 & 0.998 & 1.07 s \\ \cline{2-8}  & NB & 0.822 & 0.958 & 0.885 & 0.794 & 0.781 & 1.47 s \\ \cline{2-8}  & C4.5 & 0.992 & 0.999 & 0.999 & 0.991 & 0.995 & 11.41 s \\ \cline{2-8}  & KNN & 0.992 & 0.993 & 0.992 & 0.992 & 0.995 & 0.05 s \\ \cline{2-8}  & RF & 0.996 & 0.996 & 0.996 & 0.985 & 0.994 & 35.66 s \\ \cline{2-8}  & Ada & 0.945 & 0.945 & 0.945 & 0.945 & 0.939 & 0.26 s \\ \hline \multirow{6}{*}{Bigram} & SMO & 0.969 & 0.964 & 0.966 & 0.966 & 0.955 & 4.01 s \\ \cline{2-8}  & NB & 0.942 & 0.939 & 0.940 & 0.938 & 0.922 & 6.16 s \\ \cline{2-8}  & C4.5 & 0.965 & 0.963 & 0.964 & 0.961 & 0.919 & 36.94 s \\ \cline{2-8}  & KNN & 0.946 & 0.947 & 0.946 & 0.947 & 0.931 & 0.09 s \\ \cline{2-8}  & RF & 0.940 & 0.964 & 0.952 & 0.931 & 0.936 & 141.61 s \\ \cline{2-8}  & Ada & 0.964 & 0.959 & 0.961 & 0.953 & 0.961 & 6.67 s \\ \hline \multirow{6}{*}{Trigram} & SMO & 0.998 & 0.998 & 0.998 & 0.996 & 0.997 & 0.04 s \\ \cline{2-8}  & NB & 0.998 & 0.998 & 0.998 & 0.995 & 0.995 & 0.19 s \\ \cline{2-8}  & C4.5 & 0.989 & 0.989 & 0.988 & 0.983 & 0.985 & 1.04 s \\ \cline{2-8}  & KNN & 0.998 & 0.998 & 0.998 & 0.996 & 0.997 & 0.04 s \\ \cline{2-8}  & RF & 0.998 & 0.998 & 0.998 & 0.996 & 0.997 & 42.95 s \\ \cline{2-8}  & Ada & 0.998 & 0.998 & 0.998 & 0.995 & 0.997 & 0.46 s \\ \hline \multicolumn{8}{c}{_Sentiment140 (One-vs.-All)_} \\ \hline
**Language Model** & **ML Classifier** & **Recall** & **Precision** & **F1** & **AUC** & **GM** & **Time Complexity** \\ \hline \multirow{6}{*}{Unigram} & SMO & 0.996 & 0.996 & 0.996 & 0.989 & 0.996 & 295.83 s \\ \cline{2-8}  & NB & 0.950 & 0.960 & 0.955 & 0.948 & 0.948 & 48.56 s \\ \cline{2-8}  & C4.5 & 0.992 & 0.992 & 0.992 & 0.973 & 0.991 & 3345.64 s \\ \cline{2-8}  & KNN & 0.989 & 0.990 & 0.989 & 0.968 & 0.987 & 0.05 s \\ \cline{2-8}  & RF & 0.989 & 0.989 & 0.989 & 0.967 & 0.987 & 2970.03 s \\ \cline{2-8}  & Ada & 0.994 & 0.994 & 0.993 & 0.990 & 0.993 & 1865.02 s \\ \hline \multirow{6}{*}{Bigram} & SMO & 0.958 & 0.955 & 0.956 & 0.826 & 0.958 & 546.02 s \\ \cline{2-8}  & NB & 0.105 & 0.850 & 0.187 & 0.461 & 0.126 & 120.02 s \\ \cline{2-8}  & C4.5 & 0.941 & 0.938 & 0.939 & 0.874 & 0.925 & 15,467.56 s \\ \cline{2-8}  & KNN & 0.942 & 0.937 & 0.939 & 0.816 & 0.921 & 965.27 s \\ \cline{2-8}  & RF & 0.940 & 0.938 & 0.939 & 0.856 & 0.918 & 5634.67 s \\ \cline{2-8}  & Ada & 0.944 & 0.944 & 0.944 & 0.861 & 0.922 & 1259.64 s \\ \hline \multirow{6}{*}{Trigram} & SMO & 0.949 & 0.921 & 0.935 & 0.933 & 0.941 & 24 s \\ \cline{2-8}  & NB & 0.950 & 0.952 & 0.951 & 0.940 & 0.939 & 29.02 s \\ \cline{2-8}  & C4.5 & 0.950 & 0.952 & 0.951 & 0.940 & 0.942 & 128.65 s \\ \cline{2-8}  & KNN & 0.950 & 0.946 & 0.948 & 0.939 & 0.942 & 347.28 s \\ \cline{2-8}  & RF & 0.905 & 0.903 & 0.904 & 0.941 & 0.912 & 504.24 s \\ \cline{2-8}  & Ada & 0.947 & 0.947 & 0.947 & 0.939 & 0.941 & 630.32 s \\ \hline \end{tabular}
\end{table} 
Table 10: One vs. all classification performance. Our observation also showed that the unigram attributes channeled a better discriminative power to the classifiers to predict the training instances compared to bigram model for both _myPersonality_ and _Sentiment140_. The better informative representation value channeled by unigram attributes has been reported in other text-based personality investigations [88]. The salient unigram attributes that have enhanced the prediction process are also similar to approaches taken by psychologists. In general, psychologists would observe the words (e.g., adjectives) used by people to identify their personality characteristics based on the assumption that individual differences are encoded in the natural language, where the noticeable markers are more likely to be expressed in single words. This representation is known as the \"Lexical Hypothesis\" [89].