Muneton-Santa G, Escobar-Grisales D, Orlando Lopez-Pabon F, Pérez Toro PA, Orozco Arroyave JR (2022)
Publication Type: Journal article
Publication year: 2022
DOI: 10.1007/s11205-022-02883-z
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.
APA:
Muneton-Santa, G., Escobar-Grisales, D., Orlando Lopez-Pabon, F., Pérez Toro, P.A., & Orozco Arroyave, J.R. (2022). Classification of Poverty Condition Using Natural Language Processing. Social Indicators Research. https://doi.org/10.1007/s11205-022-02883-z
MLA:
Muneton-Santa, Guberney, et al. "Classification of Poverty Condition Using Natural Language Processing." Social Indicators Research (2022).
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