Abstract
Crowdfunding is an emerging financing method that project founders could obtain funding from vast investors through the online platform. Therefore, investigating the critical features of crowdfunding projects to forecast the project outcomes have become indispensable. This research draws upon some potential factors and introduces a swarm enhanced light gradient boosting machine (S-LightGBM) model to forecast the crowdfunding performance. Text mining and lexicon-based sentiment analysis methods were employed to derive the linguistic and sentiment features of project descriptions. This study compares the predictive power of logistic regression, support vector machine, light gradient boosting machine, and S-LightGBM on 5916 crowdfunding projects between 2017 and 2018. The result shows that the S-LightGBM approach achieves superior accuracy results than conventional methods. The usefulness of linguistic and sentiment features was also investigated and discussed. This research contributes to the existing research on machine learning methods and crowdfunding and provides fundraisers guidance for the presentation and illustration of innovative projects on crowdfunding platforms.
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Acknowledgement
This study is supported by National Natural Science Foundation of China (71901150), Guangdong Province Soft Science Project (2019A101002075), Guangdong Province Educational Science Plan 2019 (2019JKCY010), Guangdong Province Bachelor and Postgraduate Education Innovation Research Project (2019SFKC46).
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Geng, S., Huang, M., Wang, Z. (2020). A Swarm Enhanced Light Gradient Boosting Machine for Crowdfunding Project Outcome Prediction. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_34
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