Integrating topic modeling and word embedding to characterize violent deaths.
Proc Natl Acad Sci U S A
; 119(10): e2108801119, 2022 03 08.
Article
in 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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Violence
/
Databases, Factual
/
Homicide
/
Models, Theoretical
Type of study:
Prognostic_studies
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
Proc Natl Acad Sci U S A
Year:
2022
Type:
Article