## 6 Experimental Results
### Experimental Settings  
The ESCAPE framework has been developed to be distributed and has been implemented in Python. All the experiments have been performed on the BigData@PoliTO cluster ([[https://bigdata.polito.it/content/bigdata-cluster](https://bigdata.polito.it/content/bigdata-cluster)]([https://bigdata.polito.it/content/bigdata-cluster](https://bigdata.polito.it/content/bigdata-cluster)), accessed on 1 April 2022) running Apache Spark 2.3.0. The virtual nodes deployed for this research, the driver, and the executors, have 7 GB of main memory and a quad-core processor each. Below, we reported the default configuration for the joint-approach and the default configuration for the probabilistic approach. **Joint-Approach Configuration Setting. ** For the joint-approach, ESCAPE requires two parameters, i.e., the number of dimensions to be considered during the data-reduction phase (SVD), and the number of clusters (topics) in which to divide the collection under analysis. During the singular-value decomposition-reduction phase, the reduction parameter analyzes the trend of singular values in terms of their significance. Important dimensions are characterized by a large magnitude of the corresponding singular values, while those associated with a low singular value should be ignored in the subsequent phases. For this reason, we have decided to consider only the first \(T\) singular values for the analysis. \(T\), at most, will be equal to the rank of the document-term matrix. This parameter should be set by the analyst; however, since the number of documents for all the textual corpora analyzed is much smaller than the vocabulary used in each collection, the value \(T\) is set by ESCAPE to 20% of the number of documents. Nevertheless, the analyst can decide to change the proposed configuration, setting other values for \(T\). The second parameter that should be set is the number of topics. We have proposed a new self-tuning algorithm to automatically determine the best configuration. In ESCAPE, the default configuration for the maximum number of clusters is set to the average document length for each corpus. In fact, we have hypothesized that every word in the document belongs to, at most, a different topic. In this way, we set an upper bound for the value of the number of clusters. Still, if the average document length is greater than the number of documents in the corpus under analysis, then the value is set to the average frequency of the term. Even so, these choices can be changed by every analyst, since the framework architecture is distributed such that it is also able to analyze several solutions in parallel. **Probabilistic Model Configuration Setting. ** We recall that for the LDA probabilistic algorithm, five parameters should be set, which are the maximum number of iterations, the optimizer, the document concentration (\(\alpha\)), the topic concentration (\(\beta\)), and the number of topics (clusters) in which each corpora should be divided. Except for the last parameter, for which we have integrated a self-configuring algorithm, the other four parameters have to be set by the analyst. In ESCAPE, the maximum number of iterations that have to converge within the model has been set to be equal to 100, and the optimizer (or inference algorithm used to estimate the LDA model) has been set to be online variational Bayes. Furthermore, \(\alpha\) and \(\beta\) are set to maximize the log likelihood of the data under analysis. Since we have selected the online optimizer, the \(\alpha\) value and the \(\beta\) value should be greater than or equal to 0. For this study, the default value for this parameter is \(\alpha=50/K\), as proposed in the literature by different articles [59; 60; 61], and the value set for \(\beta\) is \(\beta=0.1\), as proposed in [59]. ESCAPE offers an automatic methodology able to select the proper number of clusters without involving the user in this decision.