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Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters

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dc.contributor.authorCarrillo Larco, Rodrigo M.
dc.contributor.authorCastillo-Cara, Manuel
dc.contributor.authorLovón Melgarejo, Jesús
dc.date.accessioned2025-10-14T13:56:05Z
dc.date.available2025-10-14T13:56:05Z
dc.date.issued2022-10-25
dc.descriptionThe registered version of this article, first published in Wellcome Open Research, is available online at the publisher's website: Taylor and Francis, F1000Research, https://doi.org/10.12688/wellcomeopenres.16867.5
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Wellcome Open Research, está disponible en línea en el sitio web del editor: Taylor and Francis, F1000Research, https://doi.org/10.12688/wellcomeopenres.16867.5
dc.descriptionThis work was supported by the Wellcome Trust Interna-tional Training Fellowship[214185/Z/18/Z to RMC-L].
dc.description.abstractBackground: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 26 terms appeared with a frequency of 0.08 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.en
dc.description.versionversión publicada
dc.identifier.citationCarrillo-Larco RM, Castillo-Cara M and Lovón-Melgarejo J. Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters [version 5; peer review: 1 approved, 4 approved with reservations]. Wellcome Open Res 2022, 6:177 (https://doi.org/10.12688/wellcomeopenres.16867.5)
dc.identifier.doihttps://doi.org/10.12688/wellcomeopenres.16867.5
dc.identifier.issn2398-502X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30408
dc.journal.titleWellcome Open Research
dc.journal.volume6
dc.language.isoen
dc.publisherTaylor and Francis, F1000Research
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject1203.04 Inteligencia artificial
dc.subject.keywordspublic healthen
dc.subject.keywordshealth policyen
dc.subject.keywordsNatural Language Processingen
dc.subject.keywordsLatin America and the Caribbeanen
dc.subject.keywordsPeruen
dc.subject.keywordsCOVID-19en
dc.titleGovernment plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chaptersen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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