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.
Highlights
In recent years, advances in new technologies have meant that companies have adopted new business models that incorporate globalization and using the Internet as a promotion tool for products and services [1]
A Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python to divide the identified topics into negative, positive, and neutral for the key factors that make a startup business successful
The User Generated Content (UGC) topics identified were related to the management tools used by startups to improve their internal processes; artificial intelligence and machine learning technologies; the attitude of the startups’ management and the team leaders; as well as the correct progression of the startup business model that should be based on sustainability and innovation
Summary
Advances in new technologies have meant that companies have adopted new business models that incorporate globalization and using the Internet as a promotion tool for products and services [1]. With the evolution of technologies since the first decade of the 21st century, these business models have been adapting to include new processes and social changes, as well as the new demands of consumers, who are increasingly supported in this new digital era where the use of new technologies has become a habit in both professional and personal worlds [2,3]. In this new Digital age, companies adopt business models that are scalable by using technologies that can help them to understand what and how their users and clients think [4]. This is defined as ‘innovation through technology’ [8]
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