Towards Well-Being Management with Automated Qualitative Data Analysis

This paper reports on using qualitative data analysis to understand aspects of the well-being of dwellers in underserved communities, by applying machine learning algorithms to identify specific themes from unstructured interview data. The work involved data translation, transcription, pre-processing as well as developing Word2Vec and FastText algorithms and ultimately a combined analysis engine. The reported experiments are conducted on field data captured from communities in India, hence offering a unique opportunity to examine automated context-based qualitative data analysis. The approach is proven feasible despite the dominant limitations on technology infrastructure and community awareness. The machine learning results identify themes from the interview data within minutes as opposed to hours of manual investigations through conventional qualitative analysis techniques. The outcomes from the analysis engine can be used for creating a grounded theory for further studies, hence facilitating an evidence-based approach to the evaluation of underserved communities.