Integrating topic modeling and word embedding to characterize violent deaths.
Proc Natl Acad Sci U S A
; 119(10): e2108801119, 2022 03 08.
Article
em En
| MEDLINE
| ID: mdl-35239440
ABSTRACT
SignificanceWe introduce an approach to identify latent topics in large-scale text data. Our approach integrates two prominent methods of computational text analysis:
topic modeling and word embedding. We apply our approach to written narratives of violent death (e.g., suicides and homicides) in the National Violent Death Reporting System (NVDRS). Many of our topics reveal aspects of violent death not captured in existing classification schemes. We also extract gender bias in the topics themselves (e.g., a topic about long guns is particularly masculine). Our findings suggest new lines of research that could contribute to reducing suicides or homicides. Our methods are broadly applicable to text data and can unlock similar information in other administrative databases.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Violência
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Bases de Dados Factuais
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Homicídio
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article