Mining Worldwide Entrepreneurs Psycholinguistic Dimensions from Twitter

Abstract

Entrepreneurs have been subjects of great interest among researchers since they are commonly recognized as important players for economic growth worldwide. A variety of studies have already been conducted, in which entrepreneurs motivations, emotions, choices, income, behavior, personality, and thinking, amongst others, were analyzed. However, little has been investigated about the interactions of entrepreneurs within the context of online social networks, and even less at a geographical level where they develop their duties. Two of the well-known organizations in the world, the Global Entrepreneurship Monitor (GEM) Consortium and the Global Entrepreneurship and Development Institute (GEDI), provide valuable insights about the entrepreneurial activity and the countries’ entrepreneurship ecosystems, based on several dimensions. Yet, none of such dimensions have taken into account the speech of entrepreneurs in social media, and the reproducibility of such studies presents considerable limitations as they are very costly and highly demanding. The present study proposes a framework to fetch users, grouped by their country of residence, from a target community in the micro-blogging platform Twitter. By using natural language processing and data mining techniques over psycholinguistic dimensions present on 219M posts, authored by 135K entrepreneurs of 65 countries, we found significant differences in their speech. Results indicate that African entrepreneurs show higher scores regarding negative emotions than the rest of the entrepreneurs’ population. In addition to this, we found that entrepreneurs from developed countries exhibit higher scores in positive emotions than other entrepreneurs in the world.