Event characteristic evolution excavation method and system based on microblogs

Abstract

The invention provides an event characteristic evolution excavation method and system based on microblogs. The method includes the steps that in a microblog time sequence, an evolution starting document set is selected, and graph models of documents are constructed on the microblog document set based on co-occurrence characteristics of vocabularies so as to obtain a knowledge network structure of events; according to literal characteristics of the vocabularies and the tendentious compatibility characteristic of the vocabularies, the microblog graph models are combined, and a micro evolution graph of characteristics of the events is constructed; clipping, segmentation and conversion are performed on the micro evolution graph of the events, and a macro evolution graph of the characteristics of the events is formed. According to the method, in the evolution law process for excavating the characteristics of the events, a graph excavating method based on a knowledge network of the events is adopted, the event characteristic evolution excavation method is improved in the succession aspect of knowledge as a whole, and interpretability of the excavating results is higher.

Description

technical field [0001] The invention relates to the fields of text mining and topic discovery and tracking, in particular to a method for event feature evolution and mining based on microblog text data. Background technique [0002] With the vigorous development of Web2.0 technology and applications in recent years, online microblog service has gradually become a new information dissemination platform with a large number of users and a large amount of information. According to the statistics of the 29th China Internet Report: By the end of December 2011, the actual number of microblog users in my country had reached 250 million, an increase of 296.0% over the end of the previous year, and the utilization rate of netizens was 48.7%. [0003] Different from social network services with strong relationships such as Facebook, the social network relationship of Weibo services is usually one-way—that is, users can follow them and receive information generated by them without autho...

AI Technical Summary

Problems solved by technology

These topic models are an effective way to express document collections, but they lack the expression of topic evolution, which leads to the topic / event evolution analysis results obtained by the above methods are nothing more than the change of word frequency or vocabulary vector over time, and there is no relationship between words. Linked information, no inheritance in terms of topic / event domain knowledge, lack of interpretability in terms of evolution

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