# Predicting the Success of Internet Social Welfare Crowfunding Based on Text Information
## 4 Predictor Analysis  
Multiple linear regression models are often used in prediction, which is represented by the relationship between the dependent variables and a set of predictor variables [62; 63]. Based on multiple linear regression, this paper carries out predictions for the success of Internet social welfare crowdfunding projects. Taking the fundraising ratio of charitable crowdfunding projects as the dependent variable Y, and the sentimental value as well as the quantity of information from the numeric data and text mining as independent variables, the influence of the independent variables on the fundraising ratio of the dependent variable was analyzed by setting up multiple linear regression equations. The regression models in this paper are as follows:  
\[\text{M1: Y}=\text{a}_{10}+\text{a}_{11}\text{X}_{1}+\text{a}_{12}\text{X}_{2} +\text{a}_{13}\text{X}_{3}+\text{a}_{14}\text{X}_{4}+\text{a}_{15}\text{X}_{5} +\text{a}_{16}\text{C}_{01}+\text{a}_{17}\text{C}_{02}+\varepsilon \tag{12}\]  
\[\text{M2: Y}=\text{a}_{20}+\text{a}_{21}\text{X}_{1}+\text{a}_{22}\text{X}_{2} +\text{a}_{23}\text{X}_{3}+\text{a}_{24}\text{X}_{4}+\text{a}_{25}\text{X}_{5} +\text{a}_{26}\text{X}_{6}+\text{a}_{27}\text{C}_{01}+\text{a}_{28}\text{C}_{02 }+\varepsilon \tag{13}\]  
\[\text{M3: Y}=\text{a}_{30}+\text{a}_{31}\text{X}_{4}+\text{a}_{32}\text{X}_{5} +\text{a}_{33}\text{C}_{01}+\text{a}_{34}\text{C}_{02}+\varepsilon \tag{14}\]  
\[\text{M4: Y}=\text{a}_{40}+\text{a}_{41}\text{X}_{4}+\text{a}_{42}\text{X}_{5} +\text{a}_{43}\text{X}_{6}+\text{a}_{44}\text{C}_{01}+\text{a}_{45}\text{C}_{02 }+\varepsilon \tag{15}\]  
Thereinto, Y represents the fundraising success ratio, X1 stands for the forwarding number, X2 is the verifying number, X3 is the fundraising goal, X4 represents the text's sentimental value, X5 is the quantity of information, X6 is the quadratic term of the information quantity, C01 is the sentimental value of the title, C02 represents the length of the title, a are the coefficients, and \(\varepsilon\) is the error term. X5 and X6 go are calculated based on the formulas and processes defined in \"Section 2.3\". This paper builds four multiple regression models for different purposes. Model 1 and Model 2 are full models, the independent variables of which include the forwardingnumber (X\({}_{1}\)), the verifying number (X\({}_{2}\)), the fundraising goal (X\({}_{3}\)), the text's sentimental value (X\({}_{4}\)), the quantity of information (X\({}_{5}\)), the sentimental value of the title (C\({}_{01}\)), and the length of the title (C\({}_{02}\)). Compared with that of Model 1, there is an added quadratic term of the information quantity (X\({}_{6}\)) in Model 2. These can fulfill two purposes. On the one hand, it can analyze the influence of all independent variables on the dependent variable; on the other hand, it can explore the connection between the quantity of information and the fundraising ratio. Without regard to fundraising goal and other variables, Model 3 and Model 4 center on the influence of the text and the title on the fundraising ratio. The independent variables of Model 3 only include the sentimental value (X\({}_{4}\)), the quantity of information (X\({}_{5}\)), the sentimental value of title (C\({}_{01}\)), and the length of the title (C\({}_{02}\)), while there is an added quadratic term for the information quantity (X\({}_{6}\)) in Model 4.