## 8 Conclusions and Future Work  
This paper has presented the ESCAPE framework (enhanced self-tuning characterization of document collections after parameter evaluation), which is able to support the user during all the phases of the analysis process tailored to textual data. ESCAPE includes three main building blocks to streamline the analytics process and to derive high-quality information in terms of well-separated and well-cohesive groups of documents characterizing the main topics in a given corpus. Firstly, the data distribution of each corpus is characterized by several statistical indices (e.g., Guiraud Index, TTR). The joint analysis of these statistical features is able to describe the lexical richness and characterize the data distribution of each collection under analysis. Then, a pre-processing phase is applied to prepare the textual content of documents for the next phases. These activities, which are performed subsequently, represent each document via the bag-of-words (BOW) representation. Using this model, a text (e.g., a sentence or a document) is represented as the bag (multi-set) of its words, disregarding grammar and even word order, but keeping multiplicity. To measure the relevance of these multiplicities, ESCAPE includes several weighting strategies, which are able to measure term relevance in the same dataset by exploiting a local weighting scheme (e.g., TF, LogTF), together with a global weighting scheme (e.g., entropy, IDF). ESCAPE automatically exploits all the possible combinations of local and global weighting schemes to suggest the ones that well-model the term relevance in the collection under analysis to the user. Since we are interested in finding the number of topics contained in a given collection of documents, in ESCAPE, we have integrated two strategies, because no strategy is universally superior. Specifically, we have integrated:  
* an algebraic model based on SVD decomposition, together with the K-means clustering algorithm (i.e., the joint-approach);
* a probabilistic model, based on the analysis of latent variables through the LDA (i.e., the probabilistic method). Each strategy has been enriched with a self-tuning methodology to automatically set the specific input parameters required by each involved algorithm. This frees the end user from the correct configuration of the input parameters, which is usually a time-consuming activity. Lastly, several user-friendly and exhaustive informative dashboards have been embedded to help the end-user to explore the results effectively and efficiently. To evaluate the quality of corpora partitions automatically discovered by ESCAPE, a variety of quality indices have been integrated into the proposed framework. Possible future extensions concern the _integration_ in ESCAPE of:  
1. _New data analytics algorithms_ to exploit other interesting models. Specifically, we are currently including: * Other _algebraic data-reduction algorithms_ (e.g., principal components analysis (PCA)) for the joint-approach, together with the exploitation of other clustering methods (e.g., hierarchical algorithm) and other _probabilistic topic modeling methods_ (e.g., probabilistic latent semantic analysis (pLSA));* _Autoencoder-based data reduction algorithms_ to compress the information of the input variables into a reduced dimensional space and then recreate the input dataset;
* More _weighting functions_ (e.g., aug-norm) to underline the relevance of specific terms in the collection;
* More _statistical indices_ to characterize the corpora distribution (e.g., [64]), and innovative strategies to extend the ability of ESCAPE to be more domain-adaptive ([65]);
* _Deep learning models_ to deal with a large set of corpora characterized by a variable data distribution. These models can be used either to improve the preprocessing phase or to facilitate the modeling task by shifting the current methods to the supervised ones;
2. A _semantic component_ (e.g., WordNet [66]) that is able to support the analyst in a double phase.