Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews
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 manual...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/9035/ http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf |
| Summary: | 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. |
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