Abstract or Introduction
This thesis examines whether a data mining approach, such as natural language processing, can help the founders of crowdfunding campaigns be more successful.
In a data mining framework 493,324 campaigns of the two popular crowdfunding platforms Kickstarter and Indiegogo were analyzed by natural language processing using different artificial neural networks to obtain the information needed by the founders. For frequently occurring categories, a reliable classification of the category was possible. For rare ones it was less precise. It was also shown that the more a founder concentrates on a specific category when setting up a campaign, the more likely it was that a campaign would be successful. A prediction of campaign success was also possible but was influenced by the nature of the data set. It was demonstrated that this approach could generate important information that could lead to a competitive advantage of the founders for most of the campaigns in the dataset.
Crowdfunding is an emerging industry which has gained considerable attention in recent years. Competition among campaigns and founders will therefore become increasingly intense. This means, that founders must gain a competitive advantage over the competitors to be successful. Data mining approaches which also include natural language processing could be suitable to assist the founders with valuable information when setting up campaigns and thus enable them to gain a competitive advantage. Especially the right categorization on a crowdfunding platform and prediction of success are important information to support the founders.
- Quote paper
- Benjamin Brummer (Author), 2021, Crowdfunding Campaigns. Success Prediction Through Natural Language Processing, Munich, GRIN Verlag, https://www.grin.com/document/1382124