Natural language processing versus rule-based text analysis: Comparing BERT score and readability indices to predict crowdfunding outcomes

C.S. Richard Chan, Charuta Pethe and Steven Skiena

Journal of Business Venturing Insights, 2021, vol. 16, issue C

Abstract: We explore how natural language processing can be applied to predict crowdfunding outcomes. Using the Bidirectional Encoder Representations from Transformers (BERT) technique, we find that crowdfunding projects that use a story section description with a higher average BERT score (indicating a lower quality of writing) tend to raise more funding than those with lower average BERT scores. In contrast, risk descriptions that have higher BERT scores tend to receive less funding and attract fewer backers. These relationships remain consistent after controlling for various traditional readability indices, highlighting the potential benefits of incorporating natural language processing techniques in entrepreneurship research.

Keywords: Natural language processing; Bidirectional encoder representations from transformers; Readability; Crowdfunding outcomes (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jobuve:v:16:y:2021:i:c:s2352673421000548

DOI: 10.1016/j.jbvi.2021.e00276

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