## 5 Findings of the Study
### Extrinsic Evaluation
#### 5.3.2 Machine Learning Classification  
Table 9 lists the machine learning classification results for multiclass distribution where the training document was comprised of all the instances categorized under Psychoticism, Extraversion and Neuroticism. In the viewpoint of language models, our experiments showed that the unigram feature channeled better discriminative information to the classifiers to attain up to 0.995 of recall, precision and F1, respectively, compared to bigram and trigram features from _Sentiment140_. In the same manner, the unigram features also well discriminated the classes on _myPersonality_ where the SMO achieved up to 0.979 of F1 and 0.968 of GM scores. The SMO classifier also achieved the highest AUC score (0.989) for the classification of _Sentiment140_ unigram features. The highest AUC score obtained by SMO seems to be in a similar range with other Big 5 based personality-based recognition studies [78; 79] and other nontextual affecting computing [80; 81]. The size of the training documents extensively influenced the vocabulary and efficiency of the algorithms. Based on this factor, the SMO seems to be preferable because the classifiers registered insignificantly better predictions with reasonable time processing compared to the other three classifiers. The steady prediction of SMO agrees with the findings of previous personality recognition studies that have examined the representation of English messages [82; 83]. As SMO aims to forward the Lagrange multipliers or alphas that satisfy the actual inherent learning process by identifying the support vectors [66], the transformation of inputs by the kernel function and optimization of subproblems minimized the computational cost for a large volume of the matrix. Apart from SMO, the KNN also registered steady performances using bigram and trigram features extracted from both _myPersonality_ and _Sentiment140_ datasets. The steady performances of KNN to predict the topics without actual training by determining the closeness amongst the \(d\) using Euclidean Distance also asserted the presence of reliable latent structures that are meaningful to the corresponded topics generated by SLDA. Furthermore, NB recorded negligible recall (0.284), AUC (0.376) and GM (0.264) for the classification using _Sentiment140_ bigram features and the similar effects also minorly featured on the experiment using _myPersonality_ unigram features. The low GM value indicates that the NB poorly predicts the positive instances, and it even seems to correctly classify the negative instances. The further investigation on the NB prediction showed that a large rate of misclassification occurred on Extraversion and Neuroticism instances because it cannot penalize the false negative from the majority classes and possibly because of the unfathomable reason of independent assumption set by the classifier. In other words, when NB assumed that features in the vocabulary are not independent, then each attribute exclusively contributes to the discriminative power towards the learning process. The assumption caused the magnitude of the weights for classes with strong feature dependencies to be larger than for classes with weak word dependencies. Thus, the magnitude of the weights for weak attributes will indirectly channel ambiguous information to the prediction process [84]. The AdaBoost classifier also comparatively achieved better predictions using bigram and trigram attributes. The good performance of AdaBoost on bigram features was also reported in detecting offensive language [82].