The invention discloses a post-loan risk
early warning system based on semantic
sentiment analysis. The post-loan risk
early warning system is characterized by comprising a
network data mining module, a semantic
sentiment analysis module, a total analysis module and a user interaction module. The
network data mining module is used for collecting
relevant information of customer enterprises from the network, wherein the
relevant information comprises one or more of news, reviews, Microblogs, reports and complaints relevant to the
client enterprises. The semantic
sentiment analysis module is used for receiving the
relevant information, analyzing the sentiment components of the relevant information and generating sentiment polarity K and sentiment intensity M. The total analysis module is used for obtaining the sentiment polarity K and the sentiment intensity M, generating the value of the sentiment polarity K and the value of the sentiment intensity M according to the source of the relevant information, and then obtaining a reliable coefficient P and an overall reliable coefficient W through calculation in sequence according to a predetermined formula. The user interaction module is used for giving a warning when the overall reliable coefficient W is smaller than a warning value. The post-loan risk
early warning system based on semantic sentiment analysis can give an early warning for great changes of the
client enterprises in time, help a
bank to manage the
client enterprises better, and effectively reduce post-loan risks.