## 5 Findings of the Study
### Performance Comparison
For instance, GNMF performed better on _myPersonality_ one vs. all distribution when the number of seed words is 40 and 50. On other hand, this experiment showed that reducing the number of seed words in SLDA and GNMF's vocabularies to a certain extent improved the coherence score of the predictive models. This indicates that both of the models lose their discriminative power to distinguish the psycholinguistics effects encoded in texts if more seed words are supplied to the modeling process, and the small number of seed words is sufficient to accelerate the learning process as the small volume could reduce the constraints of sampling space. To summarize, the SLDA and GNMF outperformed the nonseeded models as the seed guided models have the advantage of implicitly leveraging the information from seed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{_Non\(-\)Seeded Topic Model_} \\ \hline  & & \multicolumn{2}{c}{_myPersonality_} & \multicolumn{2}{c}{_Sentiment140_} \\ \hline \multirow{2}{*}{**Model**} & **Distribution** & **Perplexity** & **Coherence** & **Perplexity** & **Coherence** \\ \cline{3-6}  & \multirow{2}{*}{LDA} & Multiclass & 9.85 & 0.4976 & 7.14 & 0.4621 \\ \cline{3-6}  & & One vs. All & 12.65 & 0.4643 & 12.65 & 0.4465 \\ \hline \multirow{3}{*}{NMF} & Multiclass & 10.56 & 0.4839 & 7.35 & 0.4328 \\ \cline{3-6}  & \multirow{2}{*}{NMF} & One vs. All & 11.86 & 0.4601 & 13.81 & 0.4483 \\ \cline{3-6}  & & Multiclass & 15.61 & 0.4543 & 13.34 & 0.4254 \\ \cline{3-6}  & & One vs. All & 18.96 & 0.4471 & 14.46 & 0.4136 \\ \hline \multicolumn{6}{c}{_Seed\(-\)Guided Topic Model_} \\ \hline \hline \multicolumn{6}{c}{_myPersonality_} & \multicolumn{2}{c}{_Sentiment140_} \\ \hline \hline \multicolumn{6}{c}{**Number of seed words**} & **Model** & **Distribution** & **Perplexity** & **Coherence** & **Perplexity** & **Coherence** \\ \hline \multirow{3}{*}{50} & \multirow{3}{*}{SLDA} & Multiclass & \(-\)3.21 & 0.5112 & \(-\)3.43 & 0.5274 \\ \cline{3-6}  & & One vs. All & \(-\)3.23 & 0.5287 & \(-\)3.54 & 0.5443 \\ \cline{3-6}  & \multirow{3}{*}{GNMF} & Multiclass & \(-\)3.23 & 0.5087 & \(-\)3.46 & 0.5254 \\ \cline{3-6}  & & One vs. All & \(-\)3.27 & 0.5293 & \(-\)3.23 & 0.5467 \\ \hline \multirow{3}{*}{40} & \multirow{3}{*}{SLDA} & Multiclass & \(-\)3.13 & 0.5441 & \(-\)3.20 & 0.5751 \\ \cline{3-6}  & & One vs. All & \(-\)3.25 & 0.5824 & \(-\)3.46 & 0.6164 \\ \cline{3-6}  & & Multiclass & \(-\)3.20 & 0.5465 & \(-\)3.27 & 0.5673 \\ \cline{3-6}  & \multirow{3}{*}{GNMF} & One vs. All & \(-\)3.17 & 0.5831 & \(-\)3.49 & 0.5877 \\ \hline \multirow{3}{*}{30} & \multirow{3}{*}{SLDA} & Multiclass & \(-\)2.87 & 0.6331 & \(-\)2.93 & 0.6775 \\ \cline{3-6}  & & One vs. All & \(-\)2.31 & 0.6539 & \(-\)3.05 & 0.6643 \\ \cline{3-6}  & & Multiclass & \(-\)2.88 & 0.6231 & \(-\)2.99 & 0.6621 \\ \cline{3-6}  & & One vs. All & \(-\)2.32 & 0.6321 & \(-\)3.09 & 0.6712 \\ \hline \multirow{3}{*}{20} & \multirow{3}{*}{**SLDA**} & **Multiclass** & **\(-\)2.78** & **0.7293** & **\(-\)2.85** & **0.7824** \\ \cline{3-6}  & & **One vs. All** & **\(-\)2.03** & **0.7739** & **\(-\)2.27** & **0.7412** \\ \cline{3-6}  & & Multiclass & \(-\)2.98 & 0.6854 & \(-\)3.01 & 0.7061 \\ \cline{3-6}  & & One vs. All & 2.29 & 0.6935 & \(-\)3.05 & 0.7276 \\ \hline \multirow{3}{*}{10} & \multirow{3}{*}{SLDA} & Multiclass & \(-\)3.12 & 0.6634 & \(-\)2.99 & 0.7012 \\ \cline{3-6}  & & One vs. All & \(-\)2.78 & 0.6645 & \(-\)3.01 & 0.6943 \\ \cline{3-6}  & & Multiclass & \(-\)3.17 & 0.6212 & \(-\)3.02 & 0.6273 \\ \cline{3-6}  & & One vs. All & \(-\)2.76 & 0.6572 & \(-\)2.98 & 0.6632 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation. words to gain better statistical inference about the data. This investigation also showed that SLDA performed better than GNMF in identifying the coherence of texts in the context of PEN model traits.