Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

TL;DR

This paper explores how natural language processing can enhance the analysis of qualitative data like interviews and social media, enabling faster decision-making in development projects.

Contribution

It presents an overview of NLP techniques applied to qualitative data analysis in development, with a case study on micro-narratives for the UNDP project.

Findings

NLP can systematize qualitative data analysis effectively.

Application to micro-narratives demonstrates practical utility.

Potential for real-time decision support in development contexts.

Abstract

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.