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Classification of Poverty Condition Using Natural Language Processing

Author

Listed:
  • Guberney Muñetón-Santa

    (Universidad de Antioquia
    Universidad de Antioquia)

  • Daniel Escobar-Grisales

    (Universidad de Antioquia)

  • Felipe Orlando López-Pabón

    (Universidad de Antioquia)

  • Paula Andrea Pérez-Toro

    (Universidad de Antioquia
    Friedrich Alexander-Universität)

  • Juan Rafael Orozco-Arroyave

    (Universidad de Antioquia
    Friedrich Alexander-Universität)

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.

Suggested Citation

  • Guberney Muñetón-Santa & Daniel Escobar-Grisales & Felipe Orlando López-Pabón & Paula Andrea Pérez-Toro & Juan Rafael Orozco-Arroyave, 2022. "Classification of Poverty Condition Using Natural Language Processing," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(3), pages 1413-1435, August.
    • Handle: RePEc:spr:soinre:v:162:y:2022:i:3:d:10.1007_s11205-022-02883-z
    DOI: 10.1007/s11205-022-02883-z
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    References listed on IDEAS

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