Simplifying Text Mining Activities: Scalable and Self-Tuning Methodology for Topic Detection and Characterization

Abstract

In recent years, the number and heterogeneity of large scientific datasets have been growing steadily. Moreover, the analysis of these data collections is not a trivial task. There are many algorithms capable of analyzing large datasets, but parameters need to be set for each of them. Moreover, larger datasets also mean greater complexity. All this leads to the need to develop innovative, scalable, and parameter-free solutions. The goal of this research activity is to design and develop an automated data analysis engine that effectively and efficiently analyzes large collections of text data with minimal user intervention. Both parameter-free algorithms and self-assessment strategies have been proposed to suggest algorithms and specific parameter values for each step that characterizes the analysis pipeline. The proposed solutions have been tailored to text corpora characterized by variable term distributions and different document lengths. In particular, a new engine called ESCAPE (enhanced self-tuning characterization of document collections after parameter evaluation) has been designed and developed. ESCAPE integrates two different solutions for document clustering and topic modeling: the joint approach and the probabilistic approach. Both methods include ad hoc self-optimization strategies to configure the specific algorithm parameters. Moreover, novel visualization techniques and quality metrics have been integrated to analyze the performances of both approaches and to help domain experts interpret the discovered knowledge. Both approaches are able to correctly identify meaningful partitions of a given document corpus by grouping them according to topics.