Dynamic Restaurants Quality Mapping Using Online User Reviews

Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe f...

ver descrição completa

Detalhes bibliográficos
Autores: Grimaldi, Didier, Collins, Carly, García Acosta, Sebastián
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Recursos:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/72720
Acesso em linha:https://doi.org/10.3390/smartcities4030058
http://hdl.handle.net/10459.1/72720
Access Level:acceso abierto
Palavra-chave:OGRs
Health
Smart city
Food safety
Big data
id ES_fc7700217ac71e7c37ff0bc548331b3f
oai_identifier_str oai:repositori.udl.cat:10459.1/72720
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dynamic Restaurants Quality Mapping Using Online User ReviewsGrimaldi, DidierCollins, CarlyGarcía Acosta, SebastiánOGRsHealthSmart cityFood safetyBig dataMillions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises.MDPI2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/smartcities4030058http://hdl.handle.net/10459.1/72720reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.3390/smartcities4030058Smart Cities, 2021, vol. 4, núm. 3, p. 1104-1112cc-by (c) Grimaldi et al., 2021info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/727202026-06-24T12:42:17Z
dc.title.none.fl_str_mv Dynamic Restaurants Quality Mapping Using Online User Reviews
title Dynamic Restaurants Quality Mapping Using Online User Reviews
spellingShingle Dynamic Restaurants Quality Mapping Using Online User Reviews
Grimaldi, Didier
OGRs
Health
Smart city
Food safety
Big data
title_short Dynamic Restaurants Quality Mapping Using Online User Reviews
title_full Dynamic Restaurants Quality Mapping Using Online User Reviews
title_fullStr Dynamic Restaurants Quality Mapping Using Online User Reviews
title_full_unstemmed Dynamic Restaurants Quality Mapping Using Online User Reviews
title_sort Dynamic Restaurants Quality Mapping Using Online User Reviews
dc.creator.none.fl_str_mv Grimaldi, Didier
Collins, Carly
García Acosta, Sebastián
author Grimaldi, Didier
author_facet Grimaldi, Didier
Collins, Carly
García Acosta, Sebastián
author_role author
author2 Collins, Carly
García Acosta, Sebastián
author2_role author
author
dc.subject.none.fl_str_mv OGRs
Health
Smart city
Food safety
Big data
topic OGRs
Health
Smart city
Food safety
Big data
description Millions of users post comments to TripAdvisor daily, together with a numeric evaluation of their experience using a rating scale of between 1 and 5 stars. At the same time, inspectors dispatched by national and local authorities visit restaurant premises regularly to audit hygiene standards, safe food practices, and overall cleanliness. The purpose of our study is to analyze the use of online-generated reviews (OGRs) as a tool to complement official restaurant inspection procedures. Our case study-based approach, with the help of a Python-based scraping library, consists of collecting OGR data from TripAdvisor and comparing them to extant restaurants’ health inspection reports. Our findings reveal that a correlation does exist between OGRs and national health system scorings. In other words, OGRs were found to provide valid indicators of restaurant quality based on inspection ratings and can thus contribute to the prevention of foodborne illness among citizens in real time. The originality of the paper resides in the use of big data and social network data as a an easily accessible, zero-cost, and complementary tool in disease prevention systems. Incorporated in restaurant management dashboards, it will aid in determining what action plans are necessary to improve quality and customer experience on the premises.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3390/smartcities4030058
http://hdl.handle.net/10459.1/72720
url https://doi.org/10.3390/smartcities4030058
http://hdl.handle.net/10459.1/72720
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/smartcities4030058
Smart Cities, 2021, vol. 4, núm. 3, p. 1104-1112
dc.rights.none.fl_str_mv cc-by (c) Grimaldi et al., 2021
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Grimaldi et al., 2021
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425424742744064
score 15,812455

Registos relacionados