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

TL;DR

This paper introduces SLR-VTM, a visual text mining approach that enhances the efficiency and accuracy of primary study selection in systematic literature reviews, demonstrated through a comparative case study.

Contribution

The paper presents a novel VTM-based method for supporting study selection in SLRs, showing improved performance over manual methods.

Findings

Reduced time spent on study selection

Increased correct inclusion of studies

Positive pilot case study results

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.