We contribute to the debate on the impact that computer science methodologies can have upon entrepreneurship research . While many entrepreneurship researchers recognize the promise of computer science methodologies such as artificial intelligence, only a few use them purposefully. However, the use of computer science methodologies contributes significantly to original, rigorous theoretical and empirical research on all aspects of entrepreneurship. We have presented how natural language processing as a computer science methodology can provide beneficial insights into the field of entrepreneurship by predicting emerging trends on Twitter as a social media platform. In particular, various communication platforms provide publicly available Big Data to analyze and understand individuals' opinions. Therefore, in order to gain meaningful insights from such unstructured textual data, it is essential to rely on computer science methodologies. With our study, we highlighted that the application of computer science methodologies in entrepreneurship research enables researchers to empirically test the theoretical knowledge on a different database to conduct innovative research projects.
Petzolt, S., Radunski, A., Fox, D., Hölzle, K. (2022). Using Twitter Data as a Proxy for Trend Detection and Analysis of Small and Medium-sized Enterprises in the Digital Transformation. Accepted paper at 25. Interdisziplinäre Jahreskonferenz Zu Entrepreneurship, Innovation Und Mittelstand (G-Forum), Dresden, Germany.
Hölzle, K., Petzolt, S., Radunski, A., Fox, D., Kulik, O. (2022). Technologie-Trendreport – Identifikation von Trends fuer kleine und mittlere Unter- nehmen im digitalen Wandel — eine Analyse auf Basis von Twitterdaten. Mittelstand-Digital Zentrum Berlin. [Report]
 Wallach, H.M.: Topic modeling: Beyond bag-of-words, 977–984 (2006). https://doi.org/10.1145/1143844.1143967
 Robertson, A.M., Willett, P.: Applications of n-grams in textual information systems. Journal of Documentation 54(1), 48–67 (1998). https: //doi.org/10.1108/EUM0000000007161
 Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988). https://doi.org/10.1016/0306-4573(88)90021-0
 Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation, vol. 3, pp. 601–608 (2001)
 F ́evotte, C., Idier, J.: Algorithms for nonnegative matrix factorizationwith the β-divergence. Neural Computation 23(9), 2421–2456 (2011). https://doi.org/10.1162/NECO a 00168
 Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022)
 L ́evesque, M., Obschonka, M., Nambisan, S.: Pursuing impactfulentrepreneurship research using artificial intelligence. Entrepreneurship Theory and Practice 46(4), 803–832 (2022). https://doi.org/10.1177/1042258720927369