# Predicting the Success of Internet Social Welfare Crowfunding Based on Text Information
## 6 Conclusions, Applications, and Future Work  
### Conclusions  
Based on the method of text mining, information in the text dataset of Internet social crowdfunding projects in China, including expression factors, such as information entropy, sentimental value, and captions, as well as socializing factors, such as the number of people forwarding and the number of people confirming, was extracted to predict the success of project fundraising. For research purposes, a sentiment lexicon of text used in Internet social crowdfunding was constructed; moreover, information entropy was introduced to calculate the information quantity of the text. According to the research, compared with the information presentation of the text itself, the social networking elements of the fundraiser are key to the success of fundraising. The sentimental value of the text has a positive effect on the success of fundraising, while the effect of information quantity thereon is in a reverse U-shape. The positive effect of sentimental value supports the concerns of [PERSON] and [PERSON] (2017); a strong sentimental description in a fundraising text will increase the likelihood of receiving donations. This gives fundraisers with sentimental description skills an edge.  
Here, information entropy was introduced to calculate the function of information quantity in predicting the success of fundraising for crowdfunding projects for the public good. It was noticed that the effect of information quantity on fundraising success is in a reverse U shape. This verifies the applicability of the findings of [PERSON] (1972) about the limited information handling capacity of humans to the issue of fundraising for Internet social crowdfunding projects. Although the abundance of information presented to donors can help them better understand those in need of help, because of the limitations of human information processing, greater information quantity will not necessarily lead to a higher success rate; an appropriate supply of information is more beneficial to the success of such projects.  
According to this research, compared with the information presentation in the text itself, socializing factors, such as the number of people confirming and the number of people forwarding, have more significant effects on the success of fundraising for Internet crowdfunding projects for the social good. Social networks are the main platforms for disseminating social welfare crowdfunding information, and social factors play an important role in the success of such projects ([PERSON] et al., 2016; [PERSON] et al., 2017). This is related to the following two characteristics of such projects: being non-profit and being low in quantity. More donors chose to donate money out of love and the social validation of the person forwarding such fundraising information. In this era of information overload and distracted attention, donors will not spend too much time or energy carefully reading the text used for non-profit crowdfunding. In such a context, socializing factors seem particularly important.  
Furthermore, the predictive efficiency of four machine learning models, namely, the multiple regression model, the decision tree regression model, the random forest regression model, and the AdaBoost regression model, was verified and analyzed. It was noticed that, aside from linear regression, the other three models showed good predictive efficiency. Among them, the AdaBoost regressor showed the best efficiency, with an accuracy of up to 97.7%.