## 5 Findings of the Study
### Intrinsic Evaluation
#### 5.2.3. Seeking Ground Truth through Trait Correlation  
Based on the trait correlation criteria stated earlier, we conducted a manual inspection of the topics generated by SLDA to determine whether the generated topics correlated with the criteria stated above. Our inspection showed that SLDA can well predict the trait classes of the messages where most of the predictions matched the criteria. For example, SLDA transformed the original training instance (#Authid:c5977/71 fab7477c2 ae7b507d532130b0) \"There are so many fucked up people because there are so many fucked up marriages or lack thereof\" from myPersonality and modeled it as \"many fuck many fuck marriages lack thereof\" as well as categorized it under the Psychoticism class. In _myPersonality_ self-assessment report, this instance was labeled as having the elements of Agreeableness, Conscientious, and Openness. Based on criteria stated earlier, the SLDA has correctly labeled the instance of Psychoticism as the trait correlated to Agreeableness and Conscientiousness from the Big 5 framework. Furthermore, the original instance (#Authid:172400f46880b309 ca5e9f322 bb8f01) \"Thank you, \"PROPNAME\", for still being on the ballot so that stupid people could vote for you\" that correlated with Neuroticism and Openness in myPersonality was modeled as \"thank still ballot stupid could vote\" under the category of Neuroticism. On the other hand, the instance (#Authid:138 ac63 ec2b55 bbf48 fd19c300720 ca) \"Can anyone out there tell me...... I need scripting reference. Thank you\" corresponded to extraversion in both the myPersonality report and SLDA. Typically, the instances generated by SLDA were shorter compared to the original instance in _myPersonality_ as the instances went through the preprocessing tasks. The meaningful topics generated by SLDA showed the ground truth of topics and proof of concept of automatically categorizing the instances based on human traits. Simultaneously, the automatic labeling has also indicated the possibility of perceptional-based personality detection to be applied as an alternative to detect human traits without their psychological report. Because the SLDA displayed a promising performance, the same concept was applied in categorizing the _Sentiment140_ instances. Table 6 presents the samples of the _Sentiment140_ instances generated and the corresponding PEN traits. Based on the common-sense knowledge, emotional spectrum, and semantical representation of the samples [1], the sentences generated by the SLDA showed the merit and logical sense to be categorized under the corresponding PEN model traits. Furthermore, the observations on the topics generated by SLDA also revealed that our earlier assumptions to seeding the \(s\) based on one-to-one relationships insignificantly affects the inference process by SLDA. This reveals that SLDA well penetrates the semantical information encoded among the \begin{table}
\begin{tabular}{c c c} \hline \hline  & _MyPersonality_ & _Sentiment140_ \\ \hline
**Distribution** & **Intra** & **Intra** \\ \hline Multiclass & 0.832 & 0.771 \\ One vs. All & 0.827 & 0.764 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cosine similarity of SLDA topics. categories rather than naively considering the presence and absence of the seed words in each of the \(d\). Further exploration was carried out to identify text messages that may be aggregated to criminality by traversing the Psychoticism instances labeled by SLDA. Our analysis showed that none of the _myPersonality_ had any elements of criminality. This may be due to the size of _myPersonality_ datasets that are small and the fact that the procedures used to collect the dataset based on voluntarism were not practical, i.e., for criminals to voluntarily hand in their psychological report without prejudice [4]. Nevertheless, several instances from _Sentiment140_ that had been labeled as Psychoticism by SLDA seem to semantically denote the presence of criminality aspects.