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: | Proceeding Paper |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf |
| _version_ | 1796875688744058880 |
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| author | Kartia, Felizardo Salleh, Norsaremah Rafael, Martins Mendes, Emilia MacDonel, Stephen G. Maldonado, José C. |
| author_facet | Kartia, Felizardo Salleh, Norsaremah Rafael, Martins Mendes, Emilia MacDonel, Stephen G. Maldonado, José C. |
| author_sort | Kartia, Felizardo |
| collection | IIUM |
| description | 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. |
| first_indexed | 2024-03-05T22:42:19Z |
| format | Proceeding Paper |
| id | oai:generic.eprints.org:9035 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:42:19Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:90352015-05-26T07:07:39Z http://irep.iium.edu.my/9035/ Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews Kartia, Felizardo Salleh, Norsaremah Rafael, Martins Mendes, Emilia MacDonel, Stephen G. Maldonado, José C. Q Science (General) QA76 Computer software 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. 2011-09-22 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf Kartia, Felizardo and Salleh, Norsaremah and Rafael, Martins and Mendes, Emilia and MacDonel, Stephen G. and Maldonado, José C. (2011) Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews. In: 2011 International Symposium on Empirical Software Engineering and Measurement, 22 - 23 September 2011, Banff, Canada. |
| spellingShingle | Q Science (General) QA76 Computer software Kartia, Felizardo Salleh, Norsaremah Rafael, Martins Mendes, Emilia MacDonel, Stephen G. Maldonado, José C. Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title | Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title_full | Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title_fullStr | Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title_full_unstemmed | Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title_short | Using Visual Text Mining to Support the Study Selection in Systematic Literature Reviews |
| title_sort | using visual text mining to support the study selection in systematic literature reviews |
| topic | Q Science (General) QA76 Computer software |
| url | http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf |
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