Text Mining and Qualitative Analysis of an IT History Interview Collection

Abstract

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.

Exploring the possibilities of Thomson’s fourth paradigm transformation—The case for a multimodal approach to digital oral history?

“…The use of video has also been strongly tied to promoting greater Digital oral history towards a new research paradigm engagement with OH archives, and as an enhancement to access and 'digital storytelling', including in education (Christel et al, 2010;High, 2010;Kaufman, 2013;Gould and Gradowski, 2014). Most of the literature surveyed on the use of digital methods for analysing OH or spoken word collections focuses on various text-based analyses such as social network analysis and text-mining (McKether et al, 2009;Verd andLozares, 2014, McKether andFriese, 2016;O'Reagan and Fleming, 2018), semantic networking (Pattuelli and Miller, 2015), topic modelling and tagging, and datavisualization (Ohno et al, 2010;Paju et al, 2011;Xiao et al, 2013).…”