## 6 Experimental Results
### ESCAPE Performances
Moreover, the weights TF-IDF and TF-Entropy not only find the original major category, but are also able to find the sub-topic related to the major categories. In this way, if the analyst is interested in analyzing the dataset at a minor level of detail, he/she could use these weights, and leave the others for a grain analysis. ESCAPE is able to analyze the same dataset at different granularity levels. **Probabilistic Approach. ** Table 5 shows the results obtained using the probabilistic approach for dataset D1. As for the joint-approach, each dataset is evaluated for every single weighting scheme considered in ESCAPE, showing the top-three configurations. For each dataset under analysis, we will sum up the considerations about the effectiveness of ESCAPE in discovering good partitions, as the different weighting schemes vary. The main results obtained by ESCAPE for each textual corpus and weighting strategy are reported in Tables 5-7. Specifically, Tables 5-7 are related to the Wikipedia datasets, and Table 8 to the Twitter crisis collection. The PubMed results are explored in Table 9. Lastly, the Reuters collection is shown in Table 10.  
Since the considered weighting schemes highlight the importance of terms within the documents, it could be interesting for the analyst to understand how different weights affect the probabilistic model generated by the LDA. Specifically, for each result table, ESCAPE includes a row for each \(K\) obtained through the ToPIC-Similarity curve, together with the three well-known state-of-the-art quality indices used to explore the goodness of the statistical model generated. Different trends can be pointed out and detected from the analysis of these tables. Firstly, we can highlight a reverse linear trend between the entropy and silhouette metrics, since better clustering partitions are characterized by a high silhouette value and a small entropy one. Moreover, through the ToPIC-Similarity testing, the TF local weight usually finds, on average, a smaller number of clusters, independently of the global weight used. On the other hand, the LogTF local weight finds a large number of topics, which allows \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Weight** & **K** & **Perplexity** & **Silhouette** & **Entropy** & **Execution Time** \\ \hline \multirow{3}{*}{TF-IDF} & 3 & 8.812 & 0.772 & 0.256 & \multirow{3}{*}{40 m, 24 s} \\ \cline{2-5}  & 6 & 8.597 & 0.693 & 0.363 & \\ \cline{2-5}  & 10 & 8.482 & 0.682 & 0.395 & \\ \hline \multirow{3}{*}{TF-Entropy} & 5 & 9.072 & 0.762 & 0.282 & \multirow{3}{*}{30 m, 32 s} \\ \cline{2-5}  & 8 & 9.248 & 0.632 & 0.338 & \\ \cline{2-5}  & 9 & 9.267 & 0.631 & 0.339 & \\ \hline \multirow{3}{*}{LogTF-IDF} & 8 & 9.187 & 0.675 & 0.320 & \\ \cline{2-5}  & 17 & 9.126 & 0.637 & 0.362 & \\ \hline \multirow{3}{*}{LogTF-Entropy} & 5 & 9.912 & 0.891 & 0.100 & \multirow{3}{*}{30 m, 54 s} \\ \cline{2-5}  & 7 & 9.884 & 0.846 & 0.174 & \\ \cline{1-1} \cline{2-5}  & 11 & 9.979 & 0.951 & 0.108 & \\ \hline \multirow{3}{*}{Boolean-TF} & 4 & 6.492 & 0.697 & 0.421 & \multirow{3}{*}{44 m, 43 s} \\ \cline{1-1} \cline{2-5}  & 5 & 6.464 & 0.661 & 0.483 & \\ \cline{1-1} \cline{2-5}  & 17 & 6.420 & 0.381 & 1.090 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results for D1 through the probabilistic approach. the same dataset to be analyzed in detail, since this weight can also find some interesting subtopics within the macro-topic. From the exploitation of the global weights, several comments can be made. In fact, the Global IDF results show a better value for the perplexity index (e.g., at least 0.1 or greater) than those obtained using global entropy, even though the other quality metrics are not in line. Analyzing all the corpora using the Boolean-TF instead led to a comparison of very different solutions. This weighting scheme is able to find, using our ToPIC-Similarity curve, three numbers of topics with different values.