  Background: A systematic literature review (SLR) is a methodology used to
aggregate all relevant existing evidence to answer a research question of
interest. Although crucial, the process used to select primary studies can be
arduous, time consuming, and must often be conducted manually. Objective: We
propose a novel approach, known as 'Systematic Literature Review based on
Visual Text Mining' or simply SLR-VTM, to support the primary study selection
activity using visual text mining (VTM) techniques. Method: We conducted a case
study to compare the performance and effectiveness of four doctoral students in
selecting primary studies manually and using the SLR-VTM approach. To enable
the comparison, we also developed a VTM tool that implemented our approach. We
hypothesized that students using SLR-VTM would present improved selection
performance and effectiveness. Results: Our results show that incorporating VTM
in the SLR study selection activity reduced the time spent in this activity and
also increased the number of studies correctly included. Conclusions: Our pilot
case study presents promising results suggesting that the use of VTM may indeed
be beneficial during the study selection activity when performing an SLR.
