From Words to Themes: AI-Powered Qualitative Data Coding and Analysis

This article presents a qualitative research methodology that uses advanced artificial intelligence-based linguistic models in free-text interview coding and thematic analysis. The authors describe two strategies for inductive coding of interview transcriptions: generative coding and lexico-semantic coding. Both approaches use bottom-up logic, employing language models like ChatGPT and natural language processing techniques to automate building the codebook structure. This innovative coding technique enhances the precision and efficiency of qualitative data analysis and reasoning within thematic analysis. On the other hand, it improves the fit of the analytical model (the structure of the codebook or dictionary) to the linguistic data of the interviews, thereby increasing its effectiveness and interpretability. The article presents a semi-supervised qualitative data coding methodology using artificial intelligence algorithms. This method can potentially automate routine qualitative data coding procedures in computer-assisted data analysis, freeing the qualitative researcher’s time to focus on interpretation and theorising. Additionally, it is a step towards the development of digital qualitative sociology.