Text Mining and Survey Analysis
A multilingual NLP pipeline turning noisy open-ended survey feedback into structured, reportable themes.
Technology
RPython
Selected project note
The business context
Telecommunications service feedback included large volumes of open-ended survey responses that were difficult to analyse manually, especially across multiple South African languages and inconsistent spelling and grammar. Human-in-the-loop review processes were time-intensive and difficult to scale.
What was delivered
Developed an NLP pipeline to analyse qualitative survey responses and produce structured labelled outputs for downstream analysis. The solution applied text mining and topic-modelling techniques to categorise open-ended feedback, while handling multilingual inputs and noisy text characteristics common in customer survey data.
The practical impact
Significantly reduced manual analysis effort by converting unstructured feedback into a labelled dataset suitable for reporting and further insight generation. This improved the speed, consistency, and scalability of qualitative survey analysis.