## 5 Findings of the Study
### Performance Comparison  
Prior to evaluating the topics generated by SLDA, we used three nonseeded models, namely LDA [40], NMF [55], and LSA [55] as well as a seed-guided model called GNMF [42] to make comparisons with our proposed model. We also conducted an analysis to determine the sensitivity of seed words against the performance of SLDA and GNMF. The LDA, NMF and LSA were chosen due to their popularity in topic modeling and affective computing [38; 31; 42; 55]. The parameter settings for LDA and NMF, respectively, followed the same settings applied in SLDA and GNMF [42], whereas the settings applied in [55] were adopted for LSA. For GNMF, we followed the settings applied in [42] and evaluated the performance based on a number of high probable seed words in \(S\) as the vocabulary played an integral part in the seeded algorithms. We made some changes to the original GNMF code so that the model can produce the results based on perplexity and coherence. Theoretically, perplexity is an intrinsic metric that captures how well a model has performed on unseen data and is measured through the normalized log-likelihood mechanism. Meanwhile, the coherence score measures the degree of semantic similarity between high scoring words in the \(d\) and helps distinguish the semantical interpretation of topics based on statistical inference [56]. The measurement using coherence score is widely applied in topic model experiments [7; 57; 58; 50] due to the weaknesses of perplexity to serve the correlation between predictive likelihood and human judgement, and topic categorization through unsupervised manner does not guarantee the interpretability of their output [59]. Thus, to measure the coherence score, the cooccurrence for the given \(a\) was evaluated using the sliding window mechanism. The computation was executed using Normalized Pointwise Mutual Information between the top words, and the similarities were measured via cosine similarity [42; 59]. Given a topic \(t\) and its top \(N\) attributes \(At=\left\{a_{1}^{t},\ \ldots,\ a_{N}^{t}\right\}\), the coherence score computed through (7), where \(D(a)\) is the document frequency of attributes \(a\), and \(D\left(a_{i},\ a_{j}\right)\) is the count of co-occurrences of \(a_{i}\) and \(a_{j}\). Based on (7), the higher coherence score by SLDA would indicate better quality and reliability of the modeling process. \[C(t:A^{t})=\sum_{n=2}^{N}\sum_{l=1}^{n}log\ \frac{D\big{(}a_{n}^{t},\ a_{l}^{t} \big{)}+1}{D\big{(}a_{l}^{t}\big{)}} \tag{7}\]  
Table 4 unveils the outcome of this experiment. This experiment showed that the coherence score of nonseeded LDA, NMF and LSA for both _myPersonality_ and _Sentiment140_ is below 50. Furthermore, the nonseeded LDA outperformed other nonseeded models by attaining a coherence score of 0.4976 on _myPersonality_ multiclass distribution. The outperformance of LDA compare to NMF also can be seen in [PERSON] et al. [38], who focused on predicting the Big 5 traits. Our literature review also showed that standard LDA yielded better results compare to LSA in nonpersonality topic modeling study [55]. However, as expected, our experiment indicated that the seed-guided model performed better than nonseeded topic models where the coherence scores of SLDA and GNMF are more than 50 in all the analyses disclosed in Table 4. In addition, the higher perplexity obtained by LDA, NMF and LSA compared to SLDA and GNMF indicates that nonseeded models poorly interpret the topics aggregated to personality. The negative perplexity was due to the log space computation by the Gensim LDA package. Overall, SLDA achieved the highest coherence for both _myPersonality_ and _Sentiment140_ on multiclass and one vs. all distributions compared to the nonseeded topic models and GNMF when the vocabulary \(S\) contained 20 topical seed words. Apart from the best performance on 20 seed words, SLDA also seems to perform better than GNMF when the vocabulary contained 10 seed words. However, GNMF also insignificantly performed better than SLDA on a certain analysis.