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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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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