Explainable text-based features in predictive models of crowdfunding campaigns

Viktor Pekar*, Marina Candi, Ahmad Beltagui, Nikolaos Stylos, Wei Liu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Reward-Based Crowdfunding offers an opportunity for innovative ventures that would not be supported through traditional financing. A key problem for those seeking funding is understanding which features of a crowdfunding campaign will sway the decisions of a sufficient number of funders. Predictive models of fund-raising campaigns used in combination
with Explainable AI methods promise to provide such insights. However, previous work on Explainable AI has largely focused on quantitative structured data. In this study, our aim is to construct explainable models of human decisions based on analysis of natural language text, thus contributing to a fast-growing body of research on the use of Explainable AI for text analytics. We propose a novel method to construct predictions based on text via semantic clustering of sentences, which, compared with traditional methods using individual words and phrases, allows complex meaning contained in the text to be operationalised. Using experimental evaluation, we compare our proposed method to keyword extraction and topic modelling, which have traditionally been used in similar applications. Our results demonstrate that the sentence clustering method produces features with significant predictive power, compared to keyword-based methods and topic models, but which are much easier to interpret for human raters. We furthermore conduct a SHAP analysis of the models incorporating sentence clusters, demonstrating concrete insights into the types of natural language content that influence the outcome of crowdfunding campaigns.
Original languageEnglish
Number of pages31
JournalAnnals of Operations Research
Early online date12 Jan 2024
DOIs
Publication statusE-pub ahead of print - 12 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Keywords

  • Predictive Modelling
  • Crowdfunding
  • Natural language processing
  • Sentence embeddings
  • SHAP
  • 3D printing

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