Specific group discovery and expansion method based on microblog data

Abstract

The invention relates to a specific group discovery and expansion method based on microblog data, and belongs to the field of social network analysis and data mining. The specific group discovery and expansion method comprises the following specific steps: collecting relevant group information; carrying out information integration and mapping; aiming at text data to carry out characteristic extraction; calculating a user similarity degree; carrying out the self-detection of a category group; and extracting the attributes of the specific group, judging a category, and carrying out group expansion. The specific group discovery and expansion method artfully avoids the problem that group identification can not be carried out since data is sparse or incomplete when a network model is used, inputs large-scale data calculation and is high in stability.

Description

technical field [0001] The method involves the discovery and expansion of some specific text groups in social networks, especially the discovery and expansion of specific groups of microblog data, and belongs to the field of social network analysis and data mining. Background technique [0002] In the social network, users can independently release their own information and also see other people's shared information, and then build a social network in the virtual age. This sharing platform has the characteristics of timely sharing, real-time, interactive, etc., and also has the communication characteristics of traditional social society, and has become an integral part of people's work and life. [0003] In the microblog platform, data mining can obtain high information value for a large amount of text data generated by users. Therefore, it is necessary to use efficient data mining methods and machine learning algorithms to mine useful information and fully extract valuable...

AI Technical Summary

Problems solved by technology

[0007] Considering that the most accessible and comprehensive information in massive data is the text data information published by social users, we propose a group discovery and expansion method based on text data, which mainly performs natural language processing on user text data and finally extracts The characteristic information of the user is obtained, and modeling is carried out according to the characteristic information. Finally, cluster analysis is carried out by comparing the similarities between users, and finally the group community is obtained, and the main signs of the group are extracted to expand the group, which solves the problem of user In the case of sparse relationship link data, it is impossible to accurately perform group discovery

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