## 5 Knowledge Validation and Visualization
In addition to displaying only statistical values or technical diagrams, which are often difficult to interpret, ESCAPE proposes several plots to explore and visualize the knowledge extracted from textual corpora. Specifically, ESCAPE enriches the cluster set, discovered through both approaches, to provide information that is more human-readable and, therefore, more understandable: (i) _document-topic distribution_ and (ii) _topic-term distribution_. **Document-topic distribution** characterizes the distribution of the various topics identified within the document. It exploits the (i) _topic cohesion/separation_ and the (ii) _coarse-grained vs. fine-grained_ groups, analyzing how different weighting schemes can impact the result. In particular, (i) is based on the _t-distributed stochastic neighbor embedding (t-SNE) [56]_, for the characterization of the document distribution_. t-SNE allows representing high-dimensional data into lower dimensional maps through a non-linear transformation, suitable for human observation. Points assigned to different topics (i.e., clusters) are colored differently. (ii) carries out the analysis of the weight impact in terms of the coarse vs. fine-grained groups. To this aim, ESCAPE analyzes the correlation matrices to analyze the possible correlation between different topics. At first, documents are selected by topic, and then the dot products between all document pairs are computed. Thus, within the same macro-category, documents will be more similar to one another compared to those belonging to different categories. **Topic-term distribution** characterizes the distribution of the words within each latent topic. Specifically, ESCAPE includes the characterization of (i) the _topic-term distribution_, identifying the most relevant k words in terms of probability and frequency, and (ii) the _topic cohesion/separation_, in terms of relevant words. Task (i) extracts the most-probable top-k terms for each topic and represents them graphically using word clouds [57], which is a popular visualization of words typically associated with textual data. Lastly, for task (ii), we propose to use the graph representation to analyze the topic-term distribution. We have introduced two types of nodes: topic nodes and term nodes. The former, in green, represent the distinct topics, while the latter, in pink, represent the distinct terms within the collection under analysis. A topic is then connected through an edge with all the terms that are linked to it. To avoid links with low probability, ESCAPE extracts only the top-k most-relevant (i.e., with the high probabilities) words for each topic. This parameter could be set by the analyst; however, the default value is 20. If a word is connected with more than one topic, then the corresponding node is colored in red. By doing so, we are able to compute the connectivity of the graph to analyze the results of the topic modeling. If there is any topic that is only connected with words that are not connected with any other topic, then this topic is separated from the rest of the graph. This means that the number of clusters selected by ESCAPE can be separated into different topics. As a matter of fact, if all the words are connected to each other, all the terms have the same probability of belonging to each cluster.