## 5 Findings of the Study
### Extrinsic Evaluation
#### 5.3.2 Machine Learning Classification
The longer time complexity manifested by those tree-based classifiers for _Sentiment140_ indicates the inefficiency of the models for the training on big datasets, although the inductive network can handle the high dimensional data besides yielding a promising prediction score. Therefore, it is practical to use other algorithms such as SMO, KNN or AdaBoost instead of C4.5 and RF because the tree-based networks may encounter the bloating issue for high dimensional data [85]. Overall, the identical and good performance of the classifiers on most of the experiments using multiclass topics indicates that the models can capture and well generalize the underlying structure encoded in the document resampled through cross-validation. This can also be affirmed by observing the GM metric where most of the classifiers obtained very high GM values. In this regard, we can make the inference that topics generated by SLDA are consistent and structurally correlated with the traits because those classifiers can gain better insights about the nature of the data regardless of significant biases (i.e., overfitting). Consequently, this experiment was directed to examine the one vs. all distribution because most of the off-the-shelf classifiers performed well using multiclass distributions. Table 10 lists the performance capabilities of the six machine learners for one vs. all distribution. In this experiment, all the classifiers registered more than 0.9 of recall, F1, AUC and GM except for the prediction by NB using _myPersonality_ unigram and _Sentiment140_ bigram features. As seen previously, the poor performance of showed by NB on the _Sentiment140_ bigram feature also persisted on one vs. all classification where the model merely achieved an AUC of 0.461, 0.105 of recall and 0.126 of GM. This indicates that NB performance using _Sentiment140_ bigram features is worse than the previous multiclass classification. The unsatisfactory performance may be due to the concept of independent assumption of terms that diverted the magnitude of the weights among the features in the training classes. We believed that the underperformance of NB could be improved by embracing the ensemble technique in future. The SMO and AdaBoost presented a good, steady and consistent performance in all the n-gram features from _myPersonality_ and _Sentiment140_. Similar to multiclass classification, SMO also achieved highest AUC (0.997) and GM (0.998) on _myPersonality_ unigram features. Our literature review showed that the training using support vector machine-based classifiers and a SMOTE class balancing technique seems to yield better GM scores in certain research areas [86; 87]. In contrast with multiclass classification, C4.5 and KNN performed insignificantly better than SMO using bigram attributes of _myPersonality_ and trigram features of _Sentiment140_. Nevertheless, C4.5 and RF were less efficient based on the time complexity in comparison with KNN. The analysis of _Sentiment140_ using bigram attributes showed that the C4.5 classifier took 15,467.56 s to build the network. Upon inspecting the size of the tree generated by C4.5, the model was comprised of 367 nodes with 184 leaves. Therefore, the bloated size of the C4.5 tree caused the model to take additional time to execute pruning and make the decisions based on ratio of information gain. Besides the pitfall, C4.5 and RF also required more resources to make the decisions.