NLTK: the natural language toolkit
COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Research through design as a method for interaction design research in HCI
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The WEKA data mining software: an update
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LIBSVM: A library for support vector machines
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Launch hard or go home!: predicting the success of kickstarter campaigns
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Giving is caring: understanding donation behavior through email
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
The language that gets people to give: phrases that predict success on kickstarter
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Recommending investors for crowdfunding projects
Proceedings of the 23rd international conference on World wide web
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Creative individuals increasingly rely on online crowdfunding platforms to crowdsource funding for new ventures. For novice crowdfunding project creators, however, there are few resources to turn to for assistance in the planning of crowdfunding projects. We are building a tool for novice project creators to get feedback on their project designs. One component of this tool is a comparison to existing projects. As such, we have applied a variety of machine learning classifiers to learn the concept of a successful online crowdfunding project at the time of project launch. Currently our classifier can predict with roughly 68% accuracy, whether a project will be successful or not. The classification results will eventually power a prediction segment of the proposed feedback tool. Future work involves turning the results of the machine learning algorithms into human-readable content and integrating this content into the feedback tool.