Detecting indicators for startup business success: sentiment analysis using text data mining
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.3390/su11030917 |
Resumo: | The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technologybased startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels' requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator's and startup's geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues. |
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Detecting indicators for startup business success: sentiment analysis using text data miningSentiment analysisStartups businessSustainable startupsTechnology managementText data miningGeography, Planning and DevelopmentRenewable Energy, Sustainability and the EnvironmentManagement, Monitoring, Policy and LawSDG 7 - Affordable and Clean EnergyThe main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technologybased startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels' requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator's and startup's geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e IndustrialRUNSaura, Jose RamonPalos-Sanchez, PedroGrilo, Antonio2019-03-07T23:18:47Z2019-02-112019-02-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/su11030917eng2071-1050PURE: 11769008http://www.scopus.com/inward/record.url?scp=85061513479&partnerID=8YFLogxKhttps://doi.org/10.3390/su11030917info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:29:34Zoai:run.unl.pt:10362/62586Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:46.232202Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Detecting indicators for startup business success: sentiment analysis using text data mining |
title |
Detecting indicators for startup business success: sentiment analysis using text data mining |
spellingShingle |
Detecting indicators for startup business success: sentiment analysis using text data mining Saura, Jose Ramon Sentiment analysis Startups business Sustainable startups Technology management Text data mining Geography, Planning and Development Renewable Energy, Sustainability and the Environment Management, Monitoring, Policy and Law SDG 7 - Affordable and Clean Energy |
title_short |
Detecting indicators for startup business success: sentiment analysis using text data mining |
title_full |
Detecting indicators for startup business success: sentiment analysis using text data mining |
title_fullStr |
Detecting indicators for startup business success: sentiment analysis using text data mining |
title_full_unstemmed |
Detecting indicators for startup business success: sentiment analysis using text data mining |
title_sort |
Detecting indicators for startup business success: sentiment analysis using text data mining |
author |
Saura, Jose Ramon |
author_facet |
Saura, Jose Ramon Palos-Sanchez, Pedro Grilo, Antonio |
author_role |
author |
author2 |
Palos-Sanchez, Pedro Grilo, Antonio |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial RUN |
dc.contributor.author.fl_str_mv |
Saura, Jose Ramon Palos-Sanchez, Pedro Grilo, Antonio |
dc.subject.por.fl_str_mv |
Sentiment analysis Startups business Sustainable startups Technology management Text data mining Geography, Planning and Development Renewable Energy, Sustainability and the Environment Management, Monitoring, Policy and Law SDG 7 - Affordable and Clean Energy |
topic |
Sentiment analysis Startups business Sustainable startups Technology management Text data mining Geography, Planning and Development Renewable Energy, Sustainability and the Environment Management, Monitoring, Policy and Law SDG 7 - Affordable and Clean Energy |
description |
The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technologybased startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels' requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator's and startup's geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-07T23:18:47Z 2019-02-11 2019-02-11T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.3390/su11030917 |
url |
https://doi.org/10.3390/su11030917 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2071-1050 PURE: 11769008 http://www.scopus.com/inward/record.url?scp=85061513479&partnerID=8YFLogxK https://doi.org/10.3390/su11030917 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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