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A project that extrapolates three key factors: Enticement, Experience and Engagement and analyses hidden metrics from Kickstarter projects.

Jupyter Notebook 46.92% Python 0.45% HTML 1.88% Makefile 0.05% Shell 0.12% Batchfile 0.03% Java 50.56%

deep-learning-for-risk-analysis-of-crowdfunding-campaigns's Introduction

Deep Learning for Risk Analysis of Crowdfunding Campaigns

The Objective

A project that extrapolates three key factors: Enticement, Experience and Engagement from hidden metrics by analysing Kickstarter projects to further explore the effectiveness of content and its sentiments, rewards and their tangibility, funder belief and founder-funder engagement in its success through analysis of textual and numeric features. To validate the discovered metrics, an Ensemble Deep-Learning model is proposed to predict campaigns with the greatest potential to achieve funding.

Find this paper at SSRN

A Graphical Abstract

Graphical Abstract

Key Presentation Slides

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Project Demo

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Todo

  • Host demo on a video platform
  • Cleanup and Reorganise Code
  • Elaborate on the workings in the notebooks

Suggested Citation

Srinivasan, Arvind and P, Akilandeswari, An Ensemble Deep Learning Approach to Explore the Impact of Enticement, Engagement and Experience in Reward Based Crowdfunding (May 31, 2020). Available at SSRN: http://dx.doi.org/10.2139/ssrn.3615176

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