Microblog topic mining method based on dynamic behaviors of heterogeneous social media users

Abstract

The invention discloses a microblog topic mining method based on heterogeneous social media user dynamic behaviors, which comprises the following steps: constructing an attribute multivariate heterogeneous dialogue network, and mining heterogeneous social context for topic detection; introducing a neighbor-level attention mechanism and an interaction-level attention mechanism to model different influences of different neighbors and different types of interaction modes on topic inference, and learning embedding of a specific view; the representations of the plurality of views are used as inputsof multi-view neural variational reasoning, and complex associations among topic semantics carried by different views are captured, so that topics with better consistency are mined.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and social media data mining, in particular to a microblog topic mining method based on dynamic behaviors of heterogeneous social media users. Background technique [0002] The emergence of social media sites (such as Sina Weibo, etc.) has greatly changed the form of content on the Internet. Weibo allows users to publish and browse information on it, and has strong social attribute functions, such as forwarding and commenting. A large amount of text data is stored on the Weibo platform, and it grows at an alarming rate every day. Microblog text content contains a large amount of information, mining topic information from it can be used for topic recommendation, emergency detection, precision marketing, etc. At present, text topic mining technology has achieved good results when applied to long text data such as news and articles. However, the length of microblog text is rel...

AI Technical Summary

Problems solved by technology

[0004] Although the above methods have achieved good performance, multiple interactions between users, such as forwarding, commenting, following, and mentioning, should be treated equally [1] , ignoring the influence of the heterogeneity and diversity of social networks on microblog topic inference

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