Abstract
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 human-developed 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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Crowston, K., Liu, X., & Allen, E. E. (2010). Machine learning and rule-based automated coding of qualitative data. In Proceedings of the ASIST Annual Meeting (Vol. 47). John Wiley and Sons Inc. https://doi.org/10.1002/meet.14504701328
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