Abstract
With the development of the times, the traditional personal credit is facing a severe test. This paper makes an exploratory study on the practical application and development of personal credit evaluation by using the MicroBlog data. According to the previous study of personal credit evaluation literature to dig out the credit-related indicators. We summed up the three major attributes of “Attributes of Demographic”, “Tweets Content”, and “User Relationship Structure”. We use support vector machine (SVM), naive Bayesian (NB), logical regression (LR) and AdaBoost classification algorithm, according to the actual problem modeling, to analysis of social network data on personal credit. Compared with other algorithms, the AUC value of AdaBoost algorithm achieves the best effect with 0.564 under the equalization setting.
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Wang, Y. et al. (2018). Applied Analysis of Social Network Data in Personal Credit Evaluation. In: Aiello, M., Yang, Y., Zou, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2018. AIMS 2018. Lecture Notes in Computer Science(), vol 10970. Springer, Cham. https://doi.org/10.1007/978-3-319-94361-9_17
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DOI: https://doi.org/10.1007/978-3-319-94361-9_17
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