Abstract
The qualitative content analysis process has traditionally been reliant on human researchers to read and code data, with limited use of automation. However, recent advances in Natural Language Processing (NLP) offer new techniques to improve the reliability and usefulness of content analysis, especially in the area of quantitative ethnography. In this paper we propose a new qualitative content analysis workflow that utilizes techniques such as contextual word embeddings and semantic search. Each of the design principles that inform this workflow are outlined and potential NLP solutions are discussed. This is followed by the description of a new prototype, currently in development, that implements elements of the workflow. The paper concludes with an outline of two proposed research studies to evaluate the effectiveness of the workflow and prototype as well as directions for future research.
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Bakharia, A., Corrin, L. (2019). Using Recent Advances in Contextual Word Embeddings to Improve the Quantitative Ethnography Workflow. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_26
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DOI: https://doi.org/10.1007/978-3-030-33232-7_26
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