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In the context of qualitative data analysis, a major challenge is that conventional methods require intensive manual labour and are often impractical to apply to large datasets. One effective way to address this issue is by integrating emerging computational methods to overcome scalability limitations. However, a critical concern for researchers is the trustworthiness of results when machine learning and natural language processing tools are used to analyse such data. We argue that confidence in the credibility and robustness of results depends on adopting a \u2019human-in-the-loop\u2019 methodology that is able to provide researchers with control over the analytical process, while retaining the benefits of using machine learning and natural language processing. With this in mind, we propose a novel methodological framework for computational grounded theory that supports the analysis of large qualitative datasets, while maintaining the rigour of established grounded theory methodologies. 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