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Finding the Keywords Affecting the Success of Crowdfunding Projects

机译:寻找影响众筹项目成功的关键词

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Because the rise of crowdfunding, entrepreneurs decrease seeking help from traditionally financial institutions, but began to get help on the Internet. Now, more than 15 million people involved and the amount of funds raised exceeded 3.9 billion US dollars. Although crowdfunding provides a new fundraising channel for entrepreneurs who need to raise funds, success in reaching the target amount is a big challenge. How to increase the success rate of fundraising projects is the most concern of all fundraisers. Most of the current researches aimed to explore the relation between the founders and the success of the project. Relatively few works focus on the impact of the description and the wording of the project to predict the success rate of fundraising. Therefore, this study will collect real fundraising projects from Kickstarter, and analyze the text description content of these projects. The feature selection method, Support Vector Machines Recursive Feature Elimination (SVM-RFE), has been employed to find key words that may affect the success of the project. Then, we'll use selected keywords to build a prediction model by utilizing Support Vector Machines (SVM) to help emerging entrepreneurs or anyone who needs to raise funds can have a higher chance of successful fundraising.
机译:由于众筹的兴起,企业家减少了向传统金融机构寻求帮助的机会,而是开始在互联网上获得帮助。如今,有超过1500万人参与其中,筹集的资金超过39亿美元。尽管众筹为需要筹集资金的企业家提供了新的筹资渠道,但成功达到目标金额仍是一个巨大的挑战。如何提高筹款项目的成功率是所有筹款者最关心的问题。当前的大多数研究旨在探索创始人与项目成功之间的关系。相对而言,很少有工作关注项目描述和措辞的影响来预测筹款成功率。因此,本研究将收集来自Kickstarter的真实筹款项目,并分析这些项目的文字描述内容。特征选择方法,支持向量机递归特征消除(SVM-RFE),已被用来寻找可能影响项目成功的关键词。然后,我们将通过支持向量机(SVM)使用选定的关键字来构建预测模型,以帮助新兴企业家或需要筹集资金的任何人都有较高的成功筹款机会。

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