Topic-based classification and identification of global trends for startup companies

To foresee global economic trends, one needs to understand the present startup companies that soon may become new market leaders. In this paper, we explore textual descriptions of more than 250 thousand startups in the Crunchbase database. We analyze the 2009-2019 period by using topic modeling. We...

ver descrição completa

Detalhes bibliográficos
Autores: Savin, Ivan|||0000-0002-9469-0510, Chukavina, Kristina, Pushkarev, Andrey
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:257172
Acesso em linha:https://ddd.uab.cat/record/257172
https://dx.doi.org/urn:doi:10.1007/s11187-022-00609-6
Access Level:acceso abierto
Palavra-chave:Crunchbase
Machine learning
Natural language processing
Investments
Entrepreneurship
id ES_dc040463d4f22e51e1fdc4b60c7f4413
oai_identifier_str oai:ddd.uab.cat:257172
network_acronym_str ES
network_name_str España
repository_id_str
spelling Topic-based classification and identification of global trends for startup companiesSavin, Ivan|||0000-0002-9469-0510Chukavina, KristinaPushkarev, AndreyCrunchbaseMachine learningNatural language processingInvestmentsEntrepreneurshipTo foresee global economic trends, one needs to understand the present startup companies that soon may become new market leaders. In this paper, we explore textual descriptions of more than 250 thousand startups in the Crunchbase database. We analyze the 2009-2019 period by using topic modeling. We propose a novel classification of startup companies free from expert bias that contains 38 topics and quantifies the weight of each of these topics for all the startups. Taking the year of establishment and geographical location of the startups into account, we measure which topics were increasing or decreasing their share over time, and which of them were predominantly present in Europe, North America, or other regions. We find that the share of startups focused on data analytics, social platforms, and financial transfers, and time management has risen, while an opposite trend is observed for mobile gaming, online news, and online social networks as well as legal and professional services. We also identify strong regional differences in topic distribution, suggesting certain concentration of the startups. For example, sustainable agriculture is presented stronger in South America and Africa, while pharmaceutics, in North America and Europe. Furthermore, we explore which pairs of topics tend to co-occur more often together, quantify how multisectoral the startups are, and which startup classes attract more investments. Finally, we compare our classification to the one existing in the Crunchbase database, demonstrating how we improve it. 22023-01-0120232023-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/257172https://dx.doi.org/urn:doi:10.1007/s11187-022-00609-6reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2571722026-06-06T12:50:31Z
dc.title.none.fl_str_mv Topic-based classification and identification of global trends for startup companies
title Topic-based classification and identification of global trends for startup companies
spellingShingle Topic-based classification and identification of global trends for startup companies
Savin, Ivan|||0000-0002-9469-0510
Crunchbase
Machine learning
Natural language processing
Investments
Entrepreneurship
title_short Topic-based classification and identification of global trends for startup companies
title_full Topic-based classification and identification of global trends for startup companies
title_fullStr Topic-based classification and identification of global trends for startup companies
title_full_unstemmed Topic-based classification and identification of global trends for startup companies
title_sort Topic-based classification and identification of global trends for startup companies
dc.creator.none.fl_str_mv Savin, Ivan|||0000-0002-9469-0510
Chukavina, Kristina
Pushkarev, Andrey
author Savin, Ivan|||0000-0002-9469-0510
author_facet Savin, Ivan|||0000-0002-9469-0510
Chukavina, Kristina
Pushkarev, Andrey
author_role author
author2 Chukavina, Kristina
Pushkarev, Andrey
author2_role author
author
dc.subject.none.fl_str_mv Crunchbase
Machine learning
Natural language processing
Investments
Entrepreneurship
topic Crunchbase
Machine learning
Natural language processing
Investments
Entrepreneurship
description To foresee global economic trends, one needs to understand the present startup companies that soon may become new market leaders. In this paper, we explore textual descriptions of more than 250 thousand startups in the Crunchbase database. We analyze the 2009-2019 period by using topic modeling. We propose a novel classification of startup companies free from expert bias that contains 38 topics and quantifies the weight of each of these topics for all the startups. Taking the year of establishment and geographical location of the startups into account, we measure which topics were increasing or decreasing their share over time, and which of them were predominantly present in Europe, North America, or other regions. We find that the share of startups focused on data analytics, social platforms, and financial transfers, and time management has risen, while an opposite trend is observed for mobile gaming, online news, and online social networks as well as legal and professional services. We also identify strong regional differences in topic distribution, suggesting certain concentration of the startups. For example, sustainable agriculture is presented stronger in South America and Africa, while pharmaceutics, in North America and Europe. Furthermore, we explore which pairs of topics tend to co-occur more often together, quantify how multisectoral the startups are, and which startup classes attract more investments. Finally, we compare our classification to the one existing in the Crunchbase database, demonstrating how we improve it.
publishDate 2023
dc.date.none.fl_str_mv 2
2023-01-01
2023
2023-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/257172
https://dx.doi.org/urn:doi:10.1007/s11187-022-00609-6
url https://ddd.uab.cat/record/257172
https://dx.doi.org/urn:doi:10.1007/s11187-022-00609-6
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421729628028928
score 15,228081

Registos relacionados