Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Jose Ramon Saura, Pedro Palos-Sanchez and Antonio Grilo
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Jose Ramon Saura: Department of Business Economics, Faculty of Social Sciences and Law, Rey Juan Carlos University, Paseo Artilleros s/n, 28032 Madrid, Spain
Pedro Palos-Sanchez: Department of Business Administration and Marketing, University of Seville, 41018 Seville, Spain
Antonio Grilo: UNIDEMI, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal

Sustainability, 2019, vol. 11, issue 3, 1-14

Abstract: 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, 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.

Keywords: startups business; technology management; sustainable startups; sentiment analysis; text data mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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