## 6 Experimental Results
### Comparison with Respect to the State-of-the-Art Techniques
t-SNE representation. LogTF-IDF weighting schema K = 8. **(c)** Dataset D8. t-SNE representation. TF-IDF weighting schema K = 5. Figure 7: Silhouette index for \(D1\), weighted via LogTF-Entropy for the joint-approach. and En-LDA, respectively). These values depict two different scenarios, whose results are shown, along with ESCAPE's, in Figure 8 through a t-SNE representation. The RPC proposes three as the optimal number of clusters. This is the same value proposed by the first solution of the ESCAPE framework. As described above, the clustering result is not bad, but some of the original topics are mixed together (_music_ and _literature_, _sports_ and _mathematics_). In this sense, ESCAPE outperforms RPC, giving more options with different granularity levels to the analyst. With the En-LDA approach, which proposes 19 as the optimal number of clusters, good partitions are identified. As a matter of fact, all the original categories of the dataset can be recovered in topics. Furthermore, the model identifies very specific topics that describe only a few documents, and it often divides the main categories into subtopics, which deal with more specific arguments compared to main ones. For instance, the En-LDA approach identifies the _opera_ and the _instruments_ topics, which both belong to the _music_ main category. The modeling is good overall, but having more topics than what is actually required does not necessarily mean having a better result. Indeed, too many topics may not be useful for the analysis since, then, the analysts have a more complex result set to consider in their work. Figure 9 offers an intuitive graphical representation of the topics identified using TF-IDF as the weighting scheme and \(K=10\). The word clouds depicted represent the main categories present in the original dataset and effectively show which are the most significant terms for summarizing the identified topics. The five missing clusters that do not appear in the representation are those that include terms referring to more detailed subtopics, and, therefore, have not been included in the figure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Weights** & **Method** & **K** & **Perpl** & **Silh** & **Entr** \\ \hline \multirow{8}{*}{**T-IDF**} & \multirow{2}{*}{**TF-IDF**} & RPC & 3 & 8.812 & 0.772 & 0.256 \\ \cline{2-6}  & & En-LDA & 19 & 8.427 & 0.621 & 0.534 \\ \cline{2-6}  & & ESCAPE & 10 & 8.482 & 0.682 & 0.395 \\ \cline{2-6}  & & RPC & 5 & 9.072 & 0.762 & 0.282 \\ \cline{2-6}  & & En-LDA & 5 & 9.072 & 0.762 & 0.282 \\ \cline{2-6}  & & ESCAPE & 5 & 9.072 & 0.762 & 0.282 \\ \cline{2-6}  & & RPC & 7 & 9.183 & 0.693 & 0.319 \\ \cline{2-6}  & & En-LDA & 16 & 9.189 & 0.553 & 0.443 \\ \cline{2-6}  & & ESCAPE & 8 & 9.187 & 0.675 & 0.320 \\ \cline{2-6}  & & RPC & 3 & 9.777 & 0.852 & 0.144 \\ \cline{2-6}  & & En-LDA & 3 & 9.777 & 0.852 & 0.144 \\ \cline{2-6}  & & ESCAPE & 7 & 9.884 & 0.846 & 0.174 \\ \cline{2-6}  & & RPC & 4 & 6.492 & 0.697 & 0.421 \\ \cline{2-6}  & & En-LDA & 20 & 6.412 & 0.661 & 1.255 \\ \cline{2-6}  & & ESCAPE & 5 & 6.464 & 0.661 & 0.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison between ESCAPE’s performance and that of other state-of-the-art methods. Figure 8: Comparison of t-SNE representations for dataset D1. A graphical comparison between solutions with different \(K\) values is offered in Figure 10, showing t-SNE representations with four different configurations. Another appropriate comparison between ESCAPE and other state-of-the-art methods should be made from the point of view of computational cost and time. Compared to En-LDA, the proposed methodology is much faster; in fact, the number of iterations to be performed in En-LDA increases substantially with the growing vocabulary of documents. Furthermore, the search for the minimum entropy value among all possible solutions with a different \(K\) means that the methodology must be calculated for all the topics in the given set.