Natural Language Processing in Entrepreneurship and Small Business Research: An Insight, Open Problems, and Implications

Popularity of Artificial Intelligence in Entrepreneurship has generally grown. Application of Natural Language Processing (NLP) has long been well-known in some areas of Business, such as Finance, Accounting, and Marketing. However, in entrepreneurship and small business research, they are new and unsaturated. In order to give insight into the various potential applications of NLP in Entrepreneurship, this paper provides a literature review/bibliometrics of the domain. The research relies on a combination of bibliometrics analysis and a narrative literature review. Analyzing 140 journal articles from the Web of Science Core Collection, we identified a greater number of NLP approaches used in each of the six discovered topic niches in the field. The intellectual field is young and dispersed, with the prediction of entrepreneurial fundraising success as the most popular topic. The most common NLP processes in the field are text mining (generally), topic modeling (especially the Latent Dirichlet Allocation), and sentiment analysis (text mining). We also present recommendations for future research directions in the field. As far as we are aware, this paper is the first review dealing with NLP techniques/processes in entrepreneurship and small business research.