AI-Powered Machine Learning Regulated Emotions in Entrepreneurial Pitching on Investors’ Funding Decision
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".