Identifying startups business opportunities from UGC on twitter chatting: an exploratory analysis - Saura, José Ramón (2021)
The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims to identify investment opportunities for investors in Indian startups by identifying key indicators that characterize the startup ecosystem in India. To this end, a three steps data mining method is developed using data mining techniques. First, a sentiment analysis (SA), a machine learning approach that classifies the topics into groups expressing feelings, is applied to a dataset. Next, we develop a Latent Dirichlet Allocation (LDA) model, a topic-modeling technique that divides the sample of n = 14.531 tweets from Twitter into topics, using user-generated content (UGC) as data. Finally, in order to identify the characteristics of each topic we apply textual analysis (TA) to identify key indicators. The originality of the present study lies in the methodological process used for data analysis. Our results also contribute to the literature on startups. The results demonstrate that the Indian startup ecosystem is influenced by areas such as fintech, innovation, crowdfunding, hardware, funds, competition, artificial intelligence, augmented reality and electronic commerce. Of note, in view of the exploratory approach of the present study, the results and implications should be taken as descriptive, rather than determining for future investments in the Indian startup ecosystem.