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    <abstract>Equity crowdfunding under U.S. Regulation Crowdfunding (Reg CF) raised over 1.4 billion across 5,969 offerings between May 2016 and December 2022 (Chang, 2024), generating a large public corpus of Form C business descriptions on SEC EDGAR. Yet regulated equity crowdfunding remains barely studied by the financial NLP community. We conduct a systematic mapping review using automated database search, independent dual-LLM screening, targeted human adjudication, and blind quality-control auditing. We screened 726 deduplicated papers and included 49 studies applying NLP directly to crowdfunding campaign text. Only one study examines U.S. Reg CF campaign text directly, using readability, with one pre-Reg-CF Title II near-neighbor using sentiment tone and description length. The available equity-crowdfunding evidence is therefore too limited and heterogeneous to support general conclusions about the strength of textual signals in regulated equity settings. We taxonomize the included studies into five NLP method families, synthesize representative cross-study evidence in the original reported units, and identify the near absence of regulated equity research across the methodological spectrum. We also discuss regulatory constraints, structured financial information, and text length as plausible but untested explanations for this gap. The annotated evidence base, screening records, and reproducibility materials are publicly available at https://github.com/jct-nlp/finNLP-crowdfunding</abstract>
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%0 Conference Proceedings
%T NLP for Crowdfunding Success Prediction: A Systematic Mapping Review Exposes an Underexplored Equity Domain
%A Liebeskind, Chaya
%A Tzur, Hana
%A Liebeskind, Shmuel
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F liebeskind-etal-2026-nlp
%X Equity crowdfunding under U.S. Regulation Crowdfunding (Reg CF) raised over 1.4 billion across 5,969 offerings between May 2016 and December 2022 (Chang, 2024), generating a large public corpus of Form C business descriptions on SEC EDGAR. Yet regulated equity crowdfunding remains barely studied by the financial NLP community. We conduct a systematic mapping review using automated database search, independent dual-LLM screening, targeted human adjudication, and blind quality-control auditing. We screened 726 deduplicated papers and included 49 studies applying NLP directly to crowdfunding campaign text. Only one study examines U.S. Reg CF campaign text directly, using readability, with one pre-Reg-CF Title II near-neighbor using sentiment tone and description length. The available equity-crowdfunding evidence is therefore too limited and heterogeneous to support general conclusions about the strength of textual signals in regulated equity settings. We taxonomize the included studies into five NLP method families, synthesize representative cross-study evidence in the original reported units, and identify the near absence of regulated equity research across the methodological spectrum. We also discuss regulatory constraints, structured financial information, and text length as plausible but untested explanations for this gap. The annotated evidence base, screening records, and reproducibility materials are publicly available at https://github.com/jct-nlp/finNLP-crowdfunding
%U https://aclanthology.org/2026.finnlp-1.22/
%P 263-273

Markdown (Informal)

[NLP for Crowdfunding Success Prediction: A Systematic Mapping Review Exposes an Underexplored Equity Domain](https://aclanthology.org/2026.finnlp-1.22/) (Liebeskind et al., FinNLP 2026)

• NLP for Crowdfunding Success Prediction: A Systematic Mapping Review Exposes an Underexplored Equity Domain (Liebeskind et al., FinNLP 2026)

ACL

• Chaya Liebeskind, Hana Tzur, and Shmuel Liebeskind. 2026. NLP for Crowdfunding Success Prediction: A Systematic Mapping Review Exposes an Underexplored Equity Domain. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 263–273, Budapest, Hungary. Association for Computational Linguistics.