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For qualitative data analysis (QDA), researchers assign codes to text segments to arrange the information into topics or concepts. These annotations facilitate information retrieval and the identification of emerging patterns in unstructured data. However, this metadata is typically not published or reused after the research. Subsequent studies with similar research questions require a new definition of codes and do not benefit from other analysts' experience. Machine learning (ML) based classification seeded with such data remains a challenging task due to the ambiguity of code definitions and the inherent subjectivity of the exercise. Previous attempts to support QDA using ML rely on linear models and only examined individual datasets that were either smaller or coded specifically for this purpose. However, we show that modern approaches effectively capture at least part of the codes' semantics and may generalize to multiple studies. We analyze the performance of multiple classifiers across three large real-world datasets. Furthermore, we propose an ML-based approach to identify semantic relations of codes in different studies to show thematic faceting, enhance retrieval of related content, or bootstrap the coding process. These are encouraging results that suggest how analysts might benefit from prior interpretation efforts, potentially yielding new insights into qualitative data. [ABSTRACT FROM AUTHOR]

This study attempts to address the 'scant attention' (to use Barney Glaser's words) given to formal grounded theory by revisiting and reformulating the concept of accuracy (interchangeably used with accurate descriptions and empirical evidence) which Glaser blames to be the reason behind grounded theorists neglecting formal grounded theorizing. A novel concept called pragmatic accuracy is introduced to meet the requirements of formal grounded theorizing. Pragmatic accuracy by supplementing inductive reasoning with abductive places methodological self-consciousness at the center of formal grounded theorizing as there is a continual need for scrutinizing the core assumptions of a researcher and those of their participants'. This study does not entirely reject Glaser's approach of shunning accuracy. Rather it teases out the contradiction between Glaser's words and practice towards qualitative descriptions and shows how he, in a coauthored formal grounded theory, recognizes the importance of empirical references and explanatory definitions. Along with Glaser, Charmaz' and Kearney's views on accuracy are also unpacked and brought in harmony with the requirements of formal grounded theorizing. A case is made through pragmatic accuracy to encourage a sustained focus on the inclusion of empirical content in an emerging formal theory to increase its applicability and generalizability. The reformulated pragmatic accuracy also offers two lengths for formal grounded theories, article-length and book-length, depending upon the requirements of the topic and the researcher. [ABSTRACT FROM AUTHOR]