Large volumes of textual data pose considerable challenges for manual qualitative analysis. We explore semi-automatic coding of textual data by leveraging Natural Language Processing (NLP). We compare the performance of humandeveloped NLP rules to those inferred by machine learning (ML) algorithms. The results suggest that NLP with ML may be useful to support researchers coding qualitative data.
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We seek to identify work practices that make Free/Libre Open Source Software (FLOSS) development teams effective. Particularly important to team effectiveness is decision making. In this paper, we report on an inductive qualitative analysis of 360 decision episodes of six FLOSS development teams. Our analysis revealed diversity in decision-making practices that seem to be related to differences in overall team characteristics and effectiveness.
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