Conceptual Organization is Revealed by Consumer Activity Patterns.
Big data
Cognition
Computational social science
Decision making
Machine learning
Journal
Computational brain & behavior
ISSN: 2522-087X
Titre abrégé: Comput Brain Behav
Pays: Switzerland
ID NLM: 101728813
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
27
5
2020
pubmed:
27
5
2020
medline:
27
5
2020
Statut:
ppublish
Résumé
Computational models using text corpora have proved useful in understanding the nature of language and human concepts. One appeal of this work is that text, such as from newspaper articles, should reflect human behaviour and conceptual organization outside the laboratory. However, texts do not directly reflect human activity, but instead serve a communicative function and are highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns. Using product co-occurrence data from nearly 1.3-m supermarket shopping baskets, we trained a topic model to learn 25 high-level concepts (or
Identifiants
pubmed: 32455337
doi: 10.1007/s42113-019-00064-9
pii: 64
pmc: PMC7235073
doi:
Types de publication
Journal Article
Langues
eng
Pagination
162-173Informations de copyright
© The Author(s) 2019.
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