Abstract :

Crowdfunding Has Emerged As An Effective Mechanism For Raising Funds By Collecting Small Contributions From A Large Number Of Individuals Through Online Platforms. However, Assessing The Sustainability And Credibility Of Crowdfunding Projects Remains A Challenge. This Paper Proposes A Machine Learning-based Crowdfunding Application That Integrates Environmental, Social, And Governance (ESG) Information To Enhance Decision-making And Transparency. The System Employs Text Mining Techniques To Extract ESG-related Features From Project Descriptions And Utilizes Ensemble Learning Algorithms, Including XGBoost, LightGBM, AdaBoost, CatBoost, And NGBoost, For Classification And Prediction. Experimental Results Demonstrate That The Incorporation Of ESG Factors Significantly Improves Model Performance, With Environmental Attributes Contributing The Most, Followed By Social And Governance Dimensions. The Proposed Framework Not Only Increases Prediction Accuracy But Also Promotes Sustainable And Responsible Funding Practices.

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1489-1496

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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