This thesis mainly concerns data driven technologies and the related potentiality to bring out, from textual data, previously unknown knowledge. The goal, for the current work case, is to extract potentially useful information from Amazon products reviews. To this aim, Knowledge Data Discovery (KDD) process, applied on textual data collection, has been tailored to Amazon reviews dataset. First of all a broad description of text mining with its benefits and pitfalls has been introduced along with existing algorithm and methodologies; then, ESCAPE engine has been studied, tailored and proposed as a not-time consuming and low-computational cost solution. The proposed tool approaches and integrates all the building blocks of KDD processes such as data processing and characterization, self-Tuning exploratory data analytics, and knowledge validation and visualization.