## 5 Experimental Results
### Comparative Evaluation of faSTNT and TextNetTopics Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accumulated**} & \multirow{2}{*}{**Words**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**Precision**} & \multicolumn{1}{c}{**Cohen’s**} \\  & & & & & & & & & & Kappa \\ \hline \multirow{8}{*}{**F1-score**} & 1 & 6 & 68.9 & 62.0 & 75.8 & 66.5 & 71.3 & 72.0 & 37.8 \\  & 2 & 12 & 73.6 & 73.0 & 74.2 & 73.4 & 77.9 & 73.9 & 47.3 \\  & 3 & 18 & 76.3 & 77.9 & 74.8 & 76.7 & 81.1 & 75.6 & 52.7 \\  & 4 & 24 & 77.8 & 82.1 & 73.6 & 78.7 & 83.7 & 75.7 & 55.7 \\  & 6 & 35.9 & 79.9 & 86.2 & 73.6 & 81.1 & 86.0 & 76.6 & 59.8 \\  & 8 & 47.9 & 81.1 & 87.5 & 74.7 & 82.2 & 88.2 & 77.6 & 62.2 \\  & 10 & 59.8 & 81.9 & 88.3 & 75.6 & 83.0 & 89.2 & 78.3 & 63.9 \\  & 12 & 71.6 & 82.9 & 89.6 & 76.1 & 83.9 & 89.9 & 79.0 & 65.7 \\  & 14 & 83.5 & 83.2 & 89.3 & 77.0 & 84.1 & 90.5 & 79.6 & 66.3 \\  & 16 & 95.5 & 83.7 & 90.0 & 77.3 & 84.6 & 90.8 & 79.9 & 67.3 \\  & 18 & 107.4 & 83.8 & 89.9 & 77.7 & 84.9 & 90.8 & 80.2 & 67.6 \\  & 20 & 119 & 84.5 & 90.1 & 79.0 & 85.3 & 91.9 & 81.1 & 69.1 \\ \hline \multirow{8}{*}{**F1-score**} & 1 & 5.5 & 68.7 & 55.8 & 80.9 & 63.3 & 71.4 & 73.7 & 36.9 \\  & 2 & 11.2 & 79.6 & 75.3 & 83.6 & 78.1 & 85.6 & 81.5 & 59.0 \\  & 3 & 16.5 & 83.1 & 80.5 & 85.6 & 82.2 & 89.7 & 84.1 & 66.2 \\  & 4 & 21.9 & 86.0 & 84.9 & 87.1 & 85.5 & 92.7 & 86.1 & 72.0 \\  & 6 & 32.9 & 89.6 & 89.3 & 89.9 & 89.3 & 95.5 & 89.3 & 79.2 \\  & 8 & 43.5 & 90.8 & 90.8 & 90.8 & 90.5 & 96.4 & 90.3 & 81.6 \\  & 10 & 54.2 & 91.5 & 91.6 & 91.4 & 91.3 & 96.9 & 91.0 & 83.0 \\  & 12 & 64.5 & 92.2 & 92.3 & 92.1 & 92.0 & 97.2 & 91.7 & 84.4 \\  & 14 & 74.7 & 92.3 & 92.8 & 91.8 & 92.1 & 97.5 & 91.5 & 84.6 \\  & 16 & 85 & 92.7 & 93.1 & 92.2 & 92.5 & 97.7 & 91.9 & 85.3 \\  & 18 & 95 & 92.6 & 93.1 & 92.1 & 92.4 & 97.8 & 91.7 & 85.2 \\  & 20 & 105 & 92.8 & 93.5 & 92.0 & 92.6 & 97.8 & 91.7 & 85.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics of faSTNT with 40% feature discarding utilizing MultiLabel and arXiv datasets. topic cluster selection process and minimizes computational overhead, making it more scalable and practical for large datasets. These enhancements collectively contribute to fastTNT's superior performance and efficiency. Figure 14: F1-score performance comparison of fastTNT and TextNetTopics when utilizing the LitCovid dataset. The maximum and the minimum F1-scores attained by each algorithm are highlighted. The circles on the line represent the number of accumulated topic clusters. Figure 13: F1-score performance comparison of fastTNT and TextNetTopics when utilizing the WOS-5736 dataset. The maximum and the minimum F1-scores attained by each algorithm are highlighted. The circles on the line represent the number of accumulated topic clusters. Figure 16: F1-score performance comparison of fastTNT and TextNetTopics when utilizing the arXiv dataset. The maximum and the minimum F1-scores attained by each algorithm are highlighted. The circles on the line represent the number of accumulated topic clusters. Figure 15: F1-score performance comparison of fastTNT and TextNetTopics when utilizing the MultiLabel dataset. The maximum and the minimum F1-scores attained by each algorithm are highlighted. The circles on the line represent the number of accumulated topic clusters. According to Tables 2 and 3, as we expand the feature set by accumulating top-ranked topic clusters, we observe an improvement in all performance measures. These results highlight the importance of the discovered topic clusters by fastNT in differentiating classes for this classification task. However, in some instances, adding more topic clusters might introduce noise into the feature set, reducing model performance. In such cases, fastNT effectively identifies the optimal number of topic clusters to include, thereby creating an optimal feature set that minimizes computational cost while maximizing performance.