## 5 Findings of the Study
### Extrinsic Evaluation  
Previously, we have demonstrated the capability of SLDA to model the unsupervised contents in the contexts of psycholinguistics. However, the motive of topic modeling is to seek the underlying topics embedded in a document in the manner of probability and without explicit training, which may produce ambiguous or less relevant topics to some extent [8; 11]. In this regard, many existing studies suggested and used topic modeling to pre-model the unstructured data through an unsupervised or semi-supervised manner before feeding the output to supervised learning [9; 11; 42; 63]. Based on this pipeline, the data science community used machine learning techniques to measure the discriminative and generalization power of the learning algorithm in order to posit how well the topic model has identified latent semantic structure that persists over time in the training document [9; 64]. Typically, the research community accesses the quality of the learned topics \(\varphi\) by examining the predictive value of learning algorithms on unseen documents partition through a cross-validation mechanism [65]. Empirically, the considerable ambiguities in topic modeling will substantially deteriorate the discriminative power and predictive value of classifiers due to false positive and false negative caused by seed words that match unrelated texts and confound the true association between topics and words in the machine learning process [43; 64]. In this sense, our team extended the experiments to measure the predictive power of machine classifiers to serve as a basis to postulate that SLDA generated meaningful topics to some extent based on PEN model traits. Because there are no adjacent personality computing works to be benchmarked with this study due to the variation in personality model, we decided to use several prominent off-the-shelf supervised classifiers namely Sequential Minimal Optimization (SMO), Naive Bayes (NB), C4.5, K-Nearest Neighbor (KNN), Random Forest (RF)., and AdaBoost [32] to predict the ground-truth topics generated by SLDA. The six classifiers were selected based on the following consideration: (1) Popularity in the area of supervised-based personality detection [1]; (2) The high dimensional unstructured data might not be linearly separable. This paragraph will briefly introduce all the classifiers stated above. SMO is a variant of SVM that optimizes a problem iteratively by splitting it into a series of subproblems using two Lagrange Multipliers [66]. Because SMO uses the kernel function to compute the dot product of the vectors in feature space, this study has applied the popular kernel function termed Polynomial [27; 28] to transform the nonlinear space into linearly separable space. NB is a probabilistic classifier based on the Bayes rules with strong independence assumptions, which determine whether the presence or absence of a certain feature of a class is related to the presence or absence of other features. The C4.5 classifier is a variant of the Decision Tree Algorithm based on the Iterative Dichotomizer learn to determine the target values of new samples based on the various attribute values in the data [67]. Generally, KNN processes the classification by predicting new data points from known data  
Figure 6: _t-SNE_ visualization for one vs. all distribution of _myPersonality_ (**a**) and _Sentiment140_ (**b**). points based on the nearest \(k\) points where the \(k\) is evaluated using the distance function. In this study, the size of \(k\) was fixed to \(l\), and the Euclidean Distance Function was embraced to evaluate the performance of KNN. RF is a supervised text classification algorithm dealing with the high dimensional text data and philosophically operate by building multitude binary-based decision trees \(\{RF(x,\ \theta j),\ j=1\dots,\}\), where \(RF\) is a meta estimator, \(x\) is an input vector, and \(\theta j\) are lists of the independent identically distributed random vectors [68].