## 1 Introduction  
At present, modern applications, from social networks such as Facebook and Twitter, to digital libraries such as Wikipedia, collect more and more textual data. Science is in a data-intensive age, in which the creation and sharing of large scientific datasets is unheard of. Indeed, the pace of data analysis has been surpassed by the pace of data generation. The text mining field focuses on the study and development of algorithms capable of finding meaningful, unknown, and hidden information from the growing collections of textual documents. Text mining tools include: (i) grouping documents with similar properties or similar content [1, 2]; (ii) topic modeling [3, 4]; (iii) classification models [5]; (iv) document summarization [6]; and text stream analysis [7]. Each data analytics activity on textual data is challenging, as it is a process with multiple steps in which the analytics pipeline must be configured in order to discover and exploit interesting knowledge from the textual data. There is no single pipeline to analyze textual data. In the literature, there are several algorithms that can solve a particular data mining task, but in most cases, no algorithm is universally superior. Various aspects affect the performance of the algorithms, such as the cardinality of the input data, its distribution, and the type of knowledge extracted (i.e.,the type of analysis to be performed). However, some steps are common to the different pipelines, such as the collection of textual data (i.e., a set of documents of interest). Once the documents are collected, appropriate preprocessing is performed. The latter involves many steps and is an important and critical task that affects the quality of the text mining results. To perform a particular phase of data analysis, there are a considerable number of algorithms, but for each one, the specific parameters need to be manually set and the results validated by a domain expert [8]. Moreover, real-text datasets are also characterized by an inherent sparseness and variable distributions, and their complexity increases with data volume. In the analytics process tailored to sparse data collections, it is necessary to transform the data appropriately in order to extract hidden insights from them and to reduce the sparseness of the problem. Furthermore, different weighting schemes (e.g., TF-IDF, LogTF-Entropy) can be used to emphasize the relevance of the terms in the collection. Nevertheless, there are several methods, and the choice depends on the experience of the domain expert. In the end, it is not trivial to obtain the best solution that, at the same time, has a reasonable execution time and proper quality results. It is necessary to devise parameter-free solutions that require less expertise in order to lighten the process of analyses of large textual data. This paper presents ESCAPE (Enhanced Self-tuning Characterization of document collections After Parameter Evaluation), a new data analytics engine based on self-tuning strategies that aims to replace the end-user in the selection of proper algorithm parameters for the whole analytics process on textual data collections. ESCAPE includes two different solutions to address document clustering and topic modeling. In each of the proposed solutions, ad hoc self-tuning strategies have been integrated to automatically configure the specific algorithm parameters, as well as the inclusion of novel visualization techniques and quality metrics to analyze the performance of the methods and help domain experts easily interpret the discovered knowledge. Specifically, ESCAPE exploits a data-reduction phase computed through latent semantic analysis, before the exploitation of the partitional K-means algorithm (named the _joint-approach_) and the probabilistic latent dirichlet allocation (named the _probabilistic approach_). The former exploits the dimensionality reduction of the document-term matrix representing each corpus, while the latter is based on learning a generative model of term distributions over topics.