Enhancing Crowdfunding Prediction Success Using Combinational Approach of Classification and NLP Techniques

Patil, Umesh (2024) Enhancing Crowdfunding Prediction Success Using Combinational Approach of Classification and NLP Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study consists of investigation of crowdfunding campaign success by integrating classification models with Natural Language Processing (NLP) techniques, specifically Term Frequency-Inverse Document Frequency (TF-IDF). Machine learning models are evaluated on both balanced and imbalanced datasets, comprehensive assessment is ensured using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The results shows that ensemble methods like AdaBoost and Random Forest outperformed which showcased in results with high accuracy and robust classification capabilities. Logistic Regression and SVM also perform effectively on both datasets but particularly in recall, whereas K-Nearest Neighbors (KNN) shows variability in precision and accuracy. This combinational approach significantly enhances predictive accuracy and offers valuable insights for optimizing crowdfunding campaigns. These findings are beneficial to entrepreneurs, investors, and crowdfunding platforms by enhancing the effectiveness and efficient funding process. The research underscores the importance of integrating numerical and textual data for more accurate predictions, contributing to the advancement of predictive modeling in crowdfunding.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Siddig, Abubakr UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	25 Aug 2025 09:26
Last Modified:	25 Aug 2025 09:26
URI:	https://norma.ncirl.ie/id/eprint/8609

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