Using Visualization and Text Mining to Improve Qualitative Analysis

Elis Montoro Hernandes, Emanuel Teodoro, Andre Di Thommazo, Sandra Fabbri

2014

Abstract

Context: Qualitative analysis is a scientific way to deeply understand qualitative data and to aid in its analysis. However, qualitative analysis is a laborious, time-consuming and subjective process. Aim: The authors propose the use of visualization and text mining to improve the qualitative analysis process. The objective of this paper is to explain how the use of visualization can support the Coding in multiple documents simultaneously, which may allow codes standardization thus making the process more efficient. Method: The Insight tool is being developed to make the proposal feasible and a feasibility study was performed to verify if the proposal offers benefits to the process and improves its results. Results: The study shows that the subjects who applied the proposal got more standardized codes and were more efficient than the ones who applied the process manually. Conclusions: The results derived from the use of visualization and text mining, even in a feasibility study, encourage proceeding with the project, which aims to combine both techniques to obtain more benefits on qualitative analysis conduction.

References

  1. Basili, G, Basili., V., Green, S. Laitenberger, O., Lanubile, F., Shull, F., Sorumgard, S., Zelkowitz, M., 1996. Packaging researcher experience to assist replication of experiments. In ISERN Meeting, Sydney, pp. 3-6.
  2. Basili, V., Caldiera, C., Rombach, H., 1996. Goal Question Metric Paradigm, In Marciniak, J., Encyclopedia of Software Engineering, London: John Wiley & Sons.
  3. Coleman, G., O'Connor, T., 2007. Using grounded theory to understand software process improvement: A study of Irish software product companies. Information and Software Technology. vol.49, n.6, p. 654-667.
  4. Cruzes, D., Dybå, T. 2011. Recommended Steps for Thematic Synthesis in Software Engineering. In International Symposium on Empirical Software Engineering and Measurement, ESEM'11, Banff, Canada, PP. 275-284
  5. Feldman, R., Sanger, J., 2007. The Text Mining Handbook. Cambridge: Cambridge Univ. Press.
  6. Gu, Q., Lago, P. Exploring service-oriented system engineering challenges: A systematic literature review. Service Oriented Computing and Applications, vol. 3, n. 3, p.171-188.
  7. Hancock, B., 2002. Trent Focus for Research and Development in Primary Health Care: An Introduction to Qualitative Research. Trent Focus.
  8. Jedlitschka, A, Ciolkowski, M., Pfahl, D., 2008. Reporting guidelines for controlled experiments in software engineering, In Shull, F., Singer, J., Sjoberg, D., Guide to Advanced Empirical Software Engineering, Eds. London: Springer.
  9. Johnson, B, Shneiderman, B., 1991. Tree-maps: a spacefilling approach to the visualization of hierarchical information structures, In IEEE Conf. on Visualization, October 1991, pp. 284-291, San Diego.
  10. Salton, G., Allan, J. 1994. Text Retrieval Using the Vector Processing Model, In 3rd Symp. Document Analysis and Information Retrieval, March 1994. Las Vegas
  11. Seaman, C., 1999. Qualitative methods in empirical studies of software engineering, IEEE Transactions on Software Engineering, vol. 25, n. 4, p.557-573.
  12. Seaman, C., 2008. Qualitative Methods. In Shull, F., Singer, J., Sjoberg, D., Guide to Advanced Empirical Software Engineering, Eds. London: Springer.
  13. Strauss, A., Corbin, J., 2008. Basics of qualitative research: techniques and procedures for developing grounded theory. Sage Publications, 3ed.
  14. Shull, F., Carver, J., Travassos, G., 2001. An empirical methodology for introducing software processes, IN European Software Eng. Conf. , September 2001, pp. 288-296, Helsinki.
  15. Wohlin, C., Runeson, P., Höst, M., 2000. Experimentation in Sof. Eng. - An Introduction, Sweden: Springer.
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Paper Citation


in Harvard Style

Montoro Hernandes E., Teodoro E., Di Thommazo A. and Fabbri S. (2014). Using Visualization and Text Mining to Improve Qualitative Analysis . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-028-4, pages 201-208. DOI: 10.5220/0004880102010208


in Bibtex Style

@conference{iceis14,
author={Elis Montoro Hernandes and Emanuel Teodoro and Andre Di Thommazo and Sandra Fabbri},
title={Using Visualization and Text Mining to Improve Qualitative Analysis},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2014},
pages={201-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004880102010208},
isbn={978-989-758-028-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Using Visualization and Text Mining to Improve Qualitative Analysis
SN - 978-989-758-028-4
AU - Montoro Hernandes E.
AU - Teodoro E.
AU - Di Thommazo A.
AU - Fabbri S.
PY - 2014
SP - 201
EP - 208
DO - 10.5220/0004880102010208