An Effective Information Extraction Method for Social Networks

Abstract

The invention provides an effective-filtering-based real information extraction method on the basis of inherent characteristics of social networks. Effective secondary characteristics are established during detection on the basis of user information, and estimation is performed with multiple layers of rules. Analysis by linear complexity algorithm is generally used during calculation, calculation resource demand is low, and time and space cost is low; and data are calculated quickly by a linearity-based calculation method during extraction, calculation cost is low, retaining final transfer recordings or not is determined through effective scores, and accountability is high. The information extraction method has more practical value in reality and has the advantages that effectiveness of massive review information in social networks is analyzed, low quality information of various themes can be filtered, and true effective information can be retained for subsequent analysis.

Description

Technical field [0001] The invention relates to a real information extraction method for network information forwarding with multiple input and multiple output degrees, and belongs to the technical application field of social network data mining. Background technique [0002] With the development of information technology, social networking has become an important and efficient information transmission platform, and more and more people participate in it. Through this platform, individuals can expand their contacts and obtain information on the Internet. The main focus of enterprises is to find consumers, analyze consumers, understand consumers, and achieve the simplest and fastest communication with consumers. Companies use data analysis to find the circle of consumers, listen to the voice of this circle, and use opinion leaders to further expand the information that the company wants to convey, radiate the entire circle, and ultimately attract more loyal consumers. [0003] ...

AI Technical Summary

Problems solved by technology

However, there are inherent problems in the information analysis of social networks. First, the social network is full of false information and low-quality information. The unified observation and understanding of all information cannot reflect real problems; secondly, due to the large number of participants, the information It is unbounded, and information can expand infinitely in the network, and even finally affect reality. Such a large amount of original information is difficult to fully understand and effectively extract, and is ultimately used to assist decision-making

Images