Identifying crowdfunding storytellers who deliver successful projects: a machine learning approach

Crowdfunding plays a key role in financial technology to provide individuals and enterprises with funding opportunities to establish start-ups and/or new busi- ness ventures. It is mainly used to link projects’ creators and backers, collect money and plan fundraising projects via social networks. This paper proposes a machine learning-enabled approach to analyse Kickstarter numerical and textual data and predict the successful funding and delivery of crowdfunding projects. It offers crowdfunding stakeholders benefits including creator credibility assess- ment, project risk reduction, and backer confidence enhancement. This research

proposes a data pre-processing approach to prepare the dataset and extract the relevant features for the predictions. Besides, it trains and compares five numer- ical machine learning classification models and three text-mining methods to find the best-fitted numerical and textual analysis approaches. According to the results, the proposed SVM model outperforms the numerical benchmarks in terms of Accuracy, Precision, Recall, F1 score, and model Training latency. Moreover, BERT gives the best results if the dataset is complex, while Word2vec works better with simple features in textual analysis.