Classification of Poverty Condition Using Natural Language Processing

Guberney Muñetón-Santa (), Daniel Escobar-Grisales, Felipe Orlando López-Pabón, Paula Andrea Pérez-Toro and Juan Rafael Orozco-Arroyave
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Guberney Muñetón-Santa: Universidad de Antioquia
Daniel Escobar-Grisales: Universidad de Antioquia
Felipe Orlando López-Pabón: Universidad de Antioquia
Paula Andrea Pérez-Toro: Universidad de Antioquia
Juan Rafael Orozco-Arroyave: Universidad de Antioquia

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2022, vol. 162, issue 3, No 18, 1413-1435

Abstract: Abstract This work introduces a methodology to classify between poor and extremely poor people through Natural Language Processing. The approach serves as a baseline to understand and classify poverty through the people’s discourses using machine learning algorithms. Based on classical and modern word vector representations we propose two strategies for document level representations: (1) document-level features based on the concatenation of descriptive statistics and (2) Gaussian mixture models. Three classification methods are systematically evaluated: Support Vector Machines, Random Forest, and Extreme Gradient Boosting. The fourth best experiments yielded around 55% of accuracy, while the embeddings based on GloVe word vectors yielded a sensitivity of 79.6% which could be of great interest for the public policy makers to accurately find people who need to be prioritized in social programs.

Keywords: Poverty; Natural language processing; Text classification; Word embedding; Document-level embedding; Machine learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11205-022-02883-z

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