AI-Powered Machine Learning Regulated Emotions in Entrepreneurial Pitching on Investors’ Funding Decision

Entrepreneurship is an emotional journey, where pitching to potential investors is the first crucial obstacle. To successfully overcome this crucial obstacle, entrepreneurs need to manage their pitching emotionally, thereby increasing the likelihood of securing funding from potential investors. Utilizing an artificial intelligence (AI) powered machine learning (ML) algorithm to conduct emotion analysis may provide the opportunity to regulate entrepreneurs’ emotions in entrepreneurial pitching. As such, Naive Bayes’ machine learning algorithm is a possible algorithm to study the emotional interplay between entrepreneurs and prospective investors during entrepreneurial pitching. However, Naive Bayes’ machine learning algorithm has a main limitation, where the value of one or more conditional probabilities may be zero, thus leading to inaccuracies in the final results. In this paper, we propose that the use of ‘Laplace smoothing’ may provide a solution to address this limitation, and we tested its applicability in the case of entrepreneurial pitching. Our findings suggest that entrepreneurs can switch the potential investors’ funding decision from negative to positive by regulating their emotions during pitching. Considering that the entrepreneurship ecosystem is expected to be substantially impacted by AI-powered ML, one of the principle contributions of this paper is to provide entrepreneurs and managers with insights on how the AI-powered ML will work using a lower number of adopted trained data. As a result, entrepreneurs and managers can have a sound understanding of the potential benefits associated with this emerging AI-powered ML technology.