Analyzing open-ended questions in the most efficient, rapid and automatic way is a significant challenge. We propose to address this issue by creating an algorithm that combines descriptive statistics with machine learning, automatically segmenting the natural language of respondents. We will illustrate the results using a study that KNACK did for Unicef during the COVID pandemic in 2020. We provide Python code.
