Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews

• Main Authors: Kartia, Felizardo, Salleh, Norsaremah, Rafael, Martins, Mendes, Emilia, MacDonel, Stephen G., Maldonado, José C.
• Format: Conference or Workshop Item
• Language: English
• Published: 2011
• Subjects: Q Science (General); QA76 Computer software
• Online Access: http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf; http://irep.iium.edu.my/9035/

Abstract— 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.