Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
This work tackles the problem of timely data-driven decision-making for development practitioners, but it is incremental as it applies existing NLP methods to a specific domain.
The paper addresses the challenge of systematically analyzing qualitative data at scale in international development by proposing the use of natural language processing, demonstrating its application with interview data from the UNDP Fragments of Impact project to enable quicker decision-making.
Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project.