A1 Refereed original research article in a scientific journal
Text Mining and Qualitative Analysis of an IT History Interview Collection
Authors: Paju P, Malmi E, Honkela T
Publication year: 2011
Journal: IFIP Advances in Information and Communication Technology
Journal name in source: HISTORY OF NORDIC COMPUTING 3
Journal acronym: IFIP ADV INF COMM TE
Volume: 350
First page : 433
Last page: 443
Number of pages: 3
ISBN: 978-3-642-23314-2
ISSN: 1868-4238
Abstract
In this paper, we explore the possibility of applying a text mining method on a large qualitative source material concerning the history of information technology in one nation. This data was collected in the Swedish documentation project "From Computing Machines to IT." We apply text mining on the interview transcripts of this Swedish documentation project. Specifically, we seek to group the interviews according to their central themes and affinities and pinpoint the most relevant interviews for specific research questions. In addition, we search for interpersonal links between the interviews. We apply a method called the "self-organizing map" that can be used to create a similarity diagram of the interviews. We then discuss the results in several contexts including the possible future uses of text mining in researching history.
In this paper, we explore the possibility of applying a text mining method on a large qualitative source material concerning the history of information technology in one nation. This data was collected in the Swedish documentation project "From Computing Machines to IT." We apply text mining on the interview transcripts of this Swedish documentation project. Specifically, we seek to group the interviews according to their central themes and affinities and pinpoint the most relevant interviews for specific research questions. In addition, we search for interpersonal links between the interviews. We apply a method called the "self-organizing map" that can be used to create a similarity diagram of the interviews. We then discuss the results in several contexts including the possible future uses of text mining in researching history.
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