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dc.creatorPalos Sánchez, Pedro Ramiroes
dc.creatorSaura, José Ramónes
dc.creatorGrilo, Antonioes
dc.date.accessioned2019-04-15T09:14:49Z
dc.date.available2019-04-15T09:14:49Z
dc.date.issued2019-02-11
dc.identifier.citationPalos Sánchez, P.R., Saura, J.R. y Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability, 11 (3), 1-14.
dc.identifier.issn2071-1050es
dc.identifier.urihttps://hdl.handle.net/11441/85637
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, technology-based 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.es
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectstartups businesses
dc.subjecttechnology managementes
dc.subjectsustainable startupses
dc.subjectsentiment analysises
dc.subjecttext data mininges
dc.titleDetecting indicators for startup business success: Sentiment analysis using text data mininges
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Economía Financiera y Dirección de Operacioneses
dc.relation.publisherversionhttps://www.mdpi.com/2071-1050/11/3/917/pdfes
idus.format.extent14es
dc.journaltitleSustainabilityes
dc.publication.volumen11es
dc.publication.issue3es
dc.publication.initialPage1es
dc.publication.endPage14es

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