Leveraging Emotional Features and Machine Learning for Predicting Startup Funding Success

Analyzing the crucial factors which help predict startups' funding amounts is important for senior executives of these firms to formulate effective business strategies, which leads to the ultimate startup success. In this research, we crawled real-world startup funding data from the well-known “AngelList” platform which disseminates information about the company profiles of startups, the specific business sectors, and potential investors. Potential investors browse the information posted on AngelList, which may in turn influence their decisions in funding certain startups. Our work aims to evaluate the bundle of factors (e.g., sentiments and emotions embedded in startup profile descriptions, startups' fundamentals, etc.) that may influence startups' funding successes. Moreover, we have examined a variety of state-of-the-art machine learning-based prediction models. In particular, we applied TextCNN, a well-known deep learning method to extract sentimental and emotional features from company profile text to enhance the startup funding prediction task. Our experimental results show that the emotion feature can significantly boost startup funding prediction performance by 12% in terms of F-score, and it is also among the key factors that influence startup funding amounts. To our best knowledge, this work represents the first successful research on examining the relationship emotions captured in company profile text and startup funding success.