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...

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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/ http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf

id	iium-9035
recordtype	eprints
spelling	iium-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 Conference or Workshop Item 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.
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General) QA76 Computer software
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
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.
format	Conference or Workshop Item
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
title	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_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_sort	using visual text mining to support the study selection in systematic literature reviews
publishDate	2011
url	http://irep.iium.edu.my/9035/ http://irep.iium.edu.my/9035/1/ESEM2011%28Salleh%29.pdf
first_indexed	2023-09-18T20:18:49Z
last_indexed	2023-09-18T20:18:49Z
_version_	1777407968895041536

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

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