worldbank/iQual
iQual is a package that leverages natural language processing to scale up interpretative qualitative analysis. It also provides methods to assess the bias, interpretability and efficiency of the machine-enhanced codes. iQual has been applied to analyse interviews on parents' aspirations for their children in Cox's Bazaar, Bangladesh.
This tool helps qualitative researchers analyze large sets of open-ended interviews more efficiently. You provide a small set of interviews you've already human-coded, and it uses natural language processing to apply those interpretations to many more documents. This is ideal for academics, social scientists, or market researchers dealing with extensive qualitative data like survey responses or interview transcripts.
Available on PyPI.
Use this if you need to extend a small set of expert-coded qualitative interpretations to a much larger dataset of text, saving significant time while maintaining interpretative nuance.
Not ideal if your dataset is very small, or if you require purely human-driven, highly nuanced interpretation without any machine assistance.
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Last pushed
Oct 25, 2025
Commits (30d)
0
Dependencies
11
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