Forecasting Post-Seed Survival Rates of U.S. Technology Startups Through Sentiment and Financial Modeling

• Adaan Ahsun

  Covenant University

  Author

Early-stage technology startups face formidable survival challenges, with approximately 70% failing within five years of operation, creating significant uncertainty for venture capital (VC) investors navigating post-seed portfolio decisions. Traditional startup success prediction models predominantly rely on structured financial indicators such as funding rounds, total capital raised, and company age, yet they consistently overlook the critical role of external market perception and public sentiment signals. This study addresses this gap by proposing and validating a multi-modal deep learning architecture that integrates structured financial data with unstructured social media sentiment features to forecast post-seed survival rates of U.S. technology startups. The proposed framework employs BERTweet for sentiment analysis of Twitter data and a deep neural network for multimodal feature fusion, achieving a classification accuracy of 92.5% and an F1 score of 0.911 for financing success prediction—substantially outperforming traditional financial-only models (73.0% accuracy) and demonstrating a 19.5 percentage point improvement in predictive performance. The findings establish that public sentiment polarity, emotional intensity, and social media engagement metrics provide significant incremental predictive value beyond conventional financial indicators. These results offer venture capitalists a replicable, data-driven decision-support tool for optimizing portfolio allocation, identifying high-potential investments, and mitigating downside risk through early detection of startups exhibiting negative market sentiment trajectories.

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Copyright (c) 2026 Adaan Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.