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Journal Article

Low Cost Text Mining as a Strategy for Qualitative Researchers  pp2-16

Jeremy Rose, Christian Lennerholt

© Apr 2017 Volume 15 Issue 1, Editor: Ann Brown, pp1 - 56

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Abstract

Advances in text mining together with the widespread adoption of the Internet have opened up new possibilities for qualitative researchers in the information systems and business and management fields. Easy access to large amounts of textual material through search engines, combined with automated techniques for analysis, promise to simplify the process of qualitative research. In practice this turns out not to be so easy. We outline a design research approach for building a five stage process for low tech, low cost text mining, which includes insights from the text mining literature and an experiment with trend analysis in business intelligence. We summarise the prototype process, and discuss the many difficulties that currently stand in the way of high quality research by this route. Despite the difficulties, the combination of low cost text mining with qualitative research is a promising methodological avenue, and we specify some future paths for this area of study.

 

Keywords: big data, business intelligence, qualitative research method, social media analysis, text mining, text analytics

 

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Journal Issue

Volume 15 Issue 1 / Apr 2017  pp1‑56

Editor: Ann Brown

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Keywords: big data, business intelligence, qualitative research method, social media analysis, text mining, text analytics, Social Physics, crowdsourcing, multicultural, multidisciplinary, collaborative research, social sciences, Knowledge Cafés, Theory refinement, Theoretical conjectures, Research Methodology, Hermeneutics, Multiple imputation by chained equations, MICE, missing data, guidelines, review, R

 

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