An Approach Based on Visual Text Mining to Support Categorization and Classification in the Systematic Mapping

Context: Systematic mapping provides an overview of a research area to assess the quantity of evidence existing on a topic of interest. In spite of its relevance, the establishment of consistent categories and classification of primary studies in these categories are manually conducted. Objective: We propose an approach, named SM-VTM (Systematic Mapping based on Visual Text Mining), to support categorization and classification stages in the systematic mapping using Visual Text Mining (VTM), aiming at reducing time and effort required in this process. Method: We established SM-VTM, selected a VTM tool and conducted a case study comparing results of two systematic mappings: one performed manually and another using our approach. Results: The results of both systematic mappings were very similar, showing the viability of SM-VTM. Furthermore, since our approach was applied using a tool, reduction of time and effort can be achieved. Conclusions: The application of VTM seems to be very relevant in the context of systematic mapping.