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arXiv Computation and Language By Alberto M. G. Saruggia, Sebastien Germano

Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework

Read the original on arXiv Computation and Language →

The paper demonstrates that textual descriptors alone can predict early‑stage startup exits, using a dataset of 7,419 startups over 20 years. By extracting 850 text‑based features and applying supervised learning, the best model (LightGBM) achieved an F1 score of 0.48, while text alone reached 0.30, confirming the predictive power of founder narratives. Feature analysis identified hyping markers—adjectives, jargon, buzzwords—as positively correlated with exit probability, and introduced a quantifiable Hyping Score for venture capital applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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