# Improving Startup Success with Text Analysis

arXiv: https://arxiv.org/abs/2312.06236 · HTML: https://mlsanity.com/paper/2312.06236/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method](https://mlsanity.com/paper/2105.03360/) (2021)
- [Where Do You Want To Invest? Predicting Startup Funding From Freely, Publicly Available Web Information](https://mlsanity.com/paper/2204.06479/) (2022)
- [Using Twitter to predict football outcomes](https://mlsanity.com/paper/1411.1243/) (2014)

## Built on by (most cited first, 4 of 4)

- [SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance](https://mlsanity.com/paper/2405.19456/) (2024)
- [DIALECTIC: A Multi-Agent System for Startup Evaluation](https://mlsanity.com/paper/2603.12274/) (2026)
- [Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach](https://mlsanity.com/paper/2601.16568/) (2026)
- [Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework](https://mlsanity.com/paper/2608.00045/) (2026)

## Abstract (arXiv)

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction. Data collected from Crunchbase, the Google Search API, and Twitter (now X) are used to predict whether a company will raise a round of funding within a fixed time horizon. Much of the new features are textual and the Twitter subset include linguistic metrics such as measures of passive voice and parts-of-speech. A total of ten machine learning models are also evaluated for best performance. The adaptable model can be used to predict funding 1-5 years into the future, with a variable cutoff threshold to favor either precision or recall. Prediction with comparable assumptions generally achieves F scores above 0.730 which outperforms previous attempts in the literature (0.531), and does so with fewer examples. Furthermore, we find that the vast majority of the performance impact comes from the top 18 of 171 features which are mostly generic company observations, including the best performing individual feature which is the free-form text description of the company.