A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Text Mining and Qualitative Analysis of an IT History Interview Collection
Tekijät: Paju P, Malmi E, Honkela T
Julkaisuvuosi: 2011
Journal: IFIP Advances in Information and Communication Technology
Tietokannassa oleva lehden nimi: HISTORY OF NORDIC COMPUTING 3
Lehden akronyymi: IFIP ADV INF COMM TE
Vuosikerta: 350
Aloitussivu: 433
Lopetussivu: 443
Sivujen määrä: 3
ISBN: 978-3-642-23314-2
ISSN: 1868-4238
Tiivistelmä
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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