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Detecting indicators for startup business success: sentiment analysis using text data mining

2019-02-11Artigo científico Acesso aberto
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dc.contributor.authorSaura, Jose Ramon
dc.contributor.authorPalos-Sanchez, Pedro
dc.contributor.authorGrilo, Antonio
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2019-03-07T23:18:47Z
dc.date.available2019-03-07T23:18:47Z
dc.date.issued2019-02-11
dc.description.abstractThe 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent843065
dc.identifier.doi10.3390/su11030917
dc.identifier.issn2071-1050
dc.identifier.otherPURE: 11769008
dc.identifier.otherPURE UUID: 570cf894-9505-4379-b846-f7621fc3b4ad
dc.identifier.otherScopus: 85061513479
dc.identifier.otherWOS: 000458929500362
dc.identifier.otherORCID: /0000-0002-6045-9994/work/55089957
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85061513479&partnerID=8YFLogxK
dc.identifier.urlhttps://www.scopus.com/pages/publications/85061513479
dc.language.isoeng
dc.peerreviewedyes
dc.subjectSentiment analysis
dc.subjectStartups business
dc.subjectSustainable startups
dc.subjectTechnology management
dc.subjectText data mining
dc.subjectGeography, Planning and Development
dc.subjectRenewable Energy, Sustainability and the Environment
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectSDG 7 - Affordable and Clean Energy
dc.titleDetecting indicators for startup business success: sentiment analysis using text data miningen
dc.typejournal article
degois.publication.issue3
degois.publication.titleSustainability
degois.publication.volume11
dspace.entity.typePublication
rcaap.rightsopenAccess

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