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

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.

A Three-Stage method for Data Text Mining: Using UGC in Business Intelligence Analysis

“…Normally, accessing and analyzing these databases is imprecise, expensive and time-consuming. [1,2] Furthermore, as the use of these technologies has become habitual for users, it has also become commonplace for users to share information about individual experiences and opinions, as well as content related to the interests of users and companies via social networks. In looking at these types of 2 of 13 data sources, several studies have analyzed the influence of the application of strategies of business intelligence (BI) on business models [1][2][3].…”

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

“…The time horizon analyzed was from 10-17 June 2019, allowing the download of a total of n = 10,786 tweets under the search term #Education. Following Sherman et al [39] and Banerjee et al [40], we used a randomized controlled process to select this term by focusing on the education sector and the proposed research questions. This process allows researchers to systematically select a sample based on the social media content-in this study, in the form of tweets with a specific hashtag.…”

Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

“…For each review, we selected the features according to the principal feature list. Other methods, such as the latent Dirichlet allocation (LDA) model presented in the work of Saura et al [54], can be used to extract the main features.…”

“…A network-based feature selection method, that is, feature relation networks (FRNs), helped to improve the performance of the classifier. Saura et al [54] identified key factors in UGC for the creation of successful start-ups by analyzing sentiments with an SVM. This method was applied to identify the start-up topics via the polarity sentiment.…”