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Classification of Poverty Condition Using Natural Language Processing

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A88HHHD7W" target="_blank" >RIV/00216208:11320/22:88HHHD7W - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/s11205-022-02883-z" target="_blank" >https://doi.org/10.1007/s11205-022-02883-z</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s11205-022-02883-z" target="_blank" >10.1007/s11205-022-02883-z</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Classification of Poverty Condition Using Natural Language Processing

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Social Indicators Research [online]

  • ISSN

    1573-0921

  • e-ISSN

    1573-0921

  • Volume of the periodical

    162

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    JP - JAPAN

  • Number of pages

    23

  • Pages from-to

    1413-1435

  • UT code for WoS article

    000752794700001

  • EID of the result in the Scopus database

    2-s2.0-85124366997