## 5 Findings of the Study
### Extrinsic Evaluation
#### 5.3.3 Confusion Matrix  
The confusion matrix tabular (Figures 7 and 8) presented the probability of true and false classification of the classifiers that attained reasonable time processing on each language model. We randomly picked several confusion matrixes of the classifiers to discuss in this section. As shown in Figure 8, the prediction probability of 0.0000 depicted in several classifications in fact implied that the number of false cases is relatively too small. For instance, the classification by SMO using trigram attributes from _Sentiment140_ (multiclass) only misclassified two Psychoticism instances as Extraversion.  
As disclosed in the previous section, we can stress that unigram features that represent the word contexts channeled better statistical properties to the prediction compared to bigram and trigram attributes. Again, this illustration is also in the line with our earlier finding that claimed there are overlapping structural relationships between the instances of Extraversion and Neuroticism in our dataset despite the two traits being rooted in opposite sentiment polarities. However, further inspection on minority class distribution shows that the prediction probability of false positive caused by the weights of bigram and trigram is significantly larger than true positive prediction. For instance, the prediction by C4.5 using bigram features in _myPersonality_ (one vs. all) yielded a false positive probability of 0.0336 that is equivalent to the misclassification of 71 instances where the figure is substantially larger than the probability of true positive (0.0118) or 202 instances. In this regard, we can infer that respective machine learning predictions substantially contributed by the features and weights derived from the not_psychoticism class. This type 1 error occurred due to the asymmetric distribution between the classes that lead to the data distortion. This can be seen obviously were the instances from not_Psychoticism dominated the majority class region.  
However, the imbalance ratio between Psychoticism and not_Psychoticism distribution also approximately implied the real-world statistics where the number of people with psychosis disorders is relatively rare and estimated to be 1% of the world population [90]. The domination of Extraversion instances (multiclass) in the majority class region also promoted the universal positivity biases of human language that simulated the access of Extraversion characteristics and promoted prosocial behaviors in SN sites [54]. On analysis  
Figure 8: Confusion Matrix of Multiclass Classification.  
Figure 7: Confusion Matrix of one vs. all Classification.  
of true positive of majority class, the probability of unigram features was insignificantly lower than the probability of bigram features. For example, SMO correctly predicted the Extraversion instances up to 0.9465 or 94.65% using bigram features from _Sentiment140_ (multiclass) compared to 0.9430 or 94.30% using unigram features from the same dataset. In addition, the probability of false positive unigram features from majority class is quite larger than the probability of false positive of bigram and trigram. This effect can be seen absolutely in multiclass classification. As such, the mixed sensitivity shown by the language models can implicitly support or undermine the classifier's performance, although the distribution of the classes is simplified. We believed that additional experiments using other evaluation metrics may provide additional insights regarding the structure of data and capability of these machine learning classifiers.