Explainable text-based features in predictive models of crowdfunding campaigns

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.

Publication DOI: https://doi.org/10.1007/s10479-023-05800-w
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School > Advanced Services Group
College of Business and Social Sciences > Aston Business School
Additional Information: Copyright © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: Predictive modelling,Crowdfunding,Natural Language Processing,Sentence embeddings,SHAP,3D printing
Publication ISSN: 1572-9338
Last Modified: 22 Jul 2024 07:34
Date Deposited: 12 Jan 2024 17:37
Full Text Link:
Related URLs: https://link.sp ... 479-023-05800-w (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-01-12
Published Online Date: 2024-01-12
Accepted Date: 2023-12-15
Authors: Pekar, Viktor (ORCID Profile 0000-0002-9664-1675)
Candi, Marina
Beltagui, Ahmad (ORCID Profile 0000-0003-2687-0971)
Stylos, Nikolaos
Liu, Wei

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