qualitative-digital

NLP-assisted qualitative coding organizes interview transcripts into analyzable units by applying sentence-level segmentation and embedding-based semantic clustering to suggest initial open codes. This process structures messy text data into a reproducible codebook for thematic analysis.

Inter-rater reliability for thematic analysis is calculated using agreement metrics like Cohen's kappa. Computing these metrics helps validate the coding scheme, flag codebook ambiguity, and ensure rigorous, reproducible qualitative findings.

Semantic clustering supports grounded theory coding by grouping sentence-level text units with similar embeddings. This isolates representative quotes and visualizes semantic cluster views, directly informing the open, axial, and selective coding phases.

Python-based processing with sentence embeddings is required to cluster similar coded units and generate an NLP-assisted initial codebook. The workflow applies UMAP or PCA with HDBSCAN fallback to ensure deterministic, reproducible outputs.

Embedding-based clustering selects representative quotes by grouping interview transcripts into semantic clusters. This identifies the most central text units within each cluster, providing visual analytics and saturation curves for qualitative rigor.