Applying computational analysis to textual data from the wild: A feminist perspective

Abstract

With technologies that afford much larger-scale data collection than previously imagined, new ways of processing and interpreting qualitative textual data are required. HCI researchers use a range of methods for interpreting the 'full range of human experience' from qualitative data, however, such approaches are not always scalable. Feminist geography seeks to explore how diverse and varied accounts of place can be understood and represented, whilst avoiding reductive classification systems. In this paper, we assess the extent to which unsupervised topic models can support such a research agenda. Drawing on literature from Feminist and Critical GIS, we present a case study analysis of a Volunteered Geographic Information dataset of reviews about breastfeeding in public spaces. We demonstrate that topic modelling can offer novel insights and nuanced interpretations of complex concepts such as privacy and be integrated into a critically reflexive feminist data analysis approach that captures and represents diverse experiences of place.