## 5 Findings of the Study
### Intrinsic Evaluation
#### 5.2.4 Word Analysis  
We also extended our analysis to gain insights into the significance of seed words being used by SLDA to predict the trait category of the instances albeit our earlier analysis showed that SLDA performed well on 20 seed words. Figures 3 and 4 illustrate the plotting of significant seed terms over \(1\times 1\) relationships across the traits. As the heatmap unveiled the coverage and intensities of sentiment-personality terms that exposed the psychological metrics encoded in text messages, we turned our focus to address the sensitivity of seed words frequency towards the topics generated by SLDA. During the training to identify the seed words that significantly improve the categorization, we found that higher frequency seed words are not necessarily becoming a cue for SLDA to better predict the topics although the word frequency simulated the exposure of individual differences [60]. This effect indicate that the coverage of high frequency seed words may be homogenously distributed over the topics. This effect is also clearly depicted in our earlier experiment where the coherence score of SLDA and GNMF degraded when the number of seed words increased. Regarding the optimal number of seed words required to generate the well interpretable topics, our experiments suggest that seed words in the average range of 17 to 25 boosted the predictive power of SLDA. Our literature review showed that the application of optimal size of seed words perhaps varies to each experiment as some news discourse analysis studies reported the optimal number of six to seven for each category of the topic [61].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** & **Instance** & **PEN Trait** \\ \hline
1 & “photovia fuck yeah skinny bitch people really” & Psychoticism \\
2 & “goin kill alicia gave fucking sickness ughhh wtf” & Psychoticism \\
3 & “really upset louisville concert cancelled scared happen wnashville” & Neuroticism \\
4 & “well had midwfe evil evil woman gave anti jab hurt like hell & Neuroticism \\  & baby think” & \\
5 & “need someone pr experience volunteer help interested helping & Extraversion \\  & save world” & \\
6 & “happy thanksgiving facebook friends family thankful wonderful” & Extraversion \\ \hline \hline \end{tabular}
\end{table}
Table 6: Samples of SLDA generated _Sentiment140_ instances and the corresponding traits.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Num** & **Instance** \\ \hline
1 & “fucking assholes poor little girl rip khyra” \\
2 & “wut hummm waitin cum power like bp” \\
3 & “swearbot shit pisé sunt cocksucker motherfucker its fart turd twatt blink said best” \\
4 & “photovia fuck yeah skinny bitch people really” \\
5 & “goin kill alicia gave fucking sickness ughhw tuf” \\
6 & “fucking assholes poor little girl rip khyra” \\ \hline \hline \end{tabular}
\end{table}
Table 7: Samples of instances connotating the criminality aspects.  
Meanwhile, Table 8 presents the probability of the top three seed words that assist the modeling inference. It is noteworthy to underline that the probability difference between the top three and other seed words depicted in Figures 3 and 4 was very subtle. Apart from that, our further investigation disclosed that SLDA was insensitive for the tuning of hyperparameters such as \(\alpha\) and \(\beta\). This finding affirms the earlier suggestion [13] that SLDA is insensitive to parameter tuning, and a small number of seed words is sufficient for the model to converge and categorize the latent topics embedded in the dataset. Hence, recall that our earlier statement saying that our seed words vocabulary is sparse seems to be well synchronized with the topics in both _myPersonality_ and _Sentiment140_ datasets.  
Figure 4: Word distribution of one vs. all distribution for _myPersonality_ and _Sentiment140_.  
Figure 3: Word distribution of Neuroticism and Extraversion for _myPersonality_ and _Sentiment140_.