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Ezegbu, Chizoba (2020) Predicting Key Success Factors for Selecting Crowdfunding Campaigns/Projects using Machine Learning Techniques: A Case Study of Reward Based Crowdfunding Platform. Masters thesis, Dublin, National College of Ireland.
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Abstract
Reward based crowdfunding platforms provide an avenue for project creators to pitch their project and source for funds in order to finance these projects. Therefore, determining the factors which could influence the successful selection and funding is considered crucial. This field of research has not only been of keen interest to the academia but also the world of finance and technology as well. In recent times, crowdfunding has gained popularity especially with the bottlenecks experienced by entrepreneurs in accessing finance from traditional banks. This popularity has enabled crowdfunding platforms to translate entrepreneurs’ creativity and innovation into reality. This study focuses on identifying the success rate and key factors that are instrumental to creators receiving funding from backers for their projects. Machine learning techniques –Random Forest, Decision Tree, Support Vector Machine (SVM) and Naïve Bayes are adopted in predicting the attributes of Kickstarter campaigns which could make projects/campaigns stand out to be selected by backers for funding. The findings of this study reveal that Random Forest has the highest accuracy and using statistical analysis (chi-square), factors such as staff pick, duration, backers count and goal have been identified as the major drivers for Kickstarter project selection as well as funding. In addition, sentiment analysis is done using the Bing - Lexicon Based Approach is conducted revealing positive and negative words that could influence the decision of backers to invest specific Kickstarter projects.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software |
| Divisions: | School of Computing > Master of Science in FinTech |
| Depositing User: | Dan English |
| Date Deposited: | 29 Jan 2021 14:32 |
| Last Modified: | 29 Jan 2021 14:32 |
| URI: | https://norma.ncirl.ie/id/eprint/4560 |
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