Microblog topic detection method based on message passing and graph prior distribution

A priori distribution and message passing technology, applied in neural learning methods, network data retrieval, other database retrieval, etc., can solve problems such as weakening the relevance of users or posts, and achieve the effect of alleviating sparsity and good coherence

Pending Publication Date: 2021-12-31
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0006] While the previous methods embed user interactions into edge representations, VAEs assume that each data point is independent
Therefore, the correlation between users or posts is weakened when computing latent semantic vectors

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  • Microblog topic detection method based on message passing and graph prior distribution
  • Microblog topic detection method based on message passing and graph prior distribution
  • Microblog topic detection method based on message passing and graph prior distribution

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Embodiment Construction

[0048] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0049] Taking the real microblog data set of 3 months as an example, the specific implementation method of the present invention is given. The algorithm flow of the whole system consists of three steps: building a user-level social network, embedding representation of user nodes based on message passing, and topic generation based on graph prior variational autoencoders, see figure 1 . Specific steps are as follows:

[0050] S1. Build a user-level social network:

[0051] Previous work on Sina Weibo collected relevant microblogs covering 50 hot topics in May, June and July 2014. In this embodiment, a user-level social network is constructed based on the microblog corpus. The spec...

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Abstract

The invention discloses a microblog topic detection method based on message passing and graph prior distribution, which comprises the following steps of: (1) on the basis of microblog corpus, constructing a user-level social network according to an interaction relationship among users; (2) performing user node embedding representation based on message passing: integrating content information and structure information of posts in social media by utilizing a graph convolutional network, and embedding an interaction relationship between users into the user node embedding representation; and (3) performing topic generation based on the graph prior variational auto-encoder: taking the user node embedded representation integrated with the user interaction relationship as input, replacing the standard Gaussian prior distribution of the variational auto-encoder with graph prior distribution containing user interaction, and considering the correlation between users in the topic inference process. In general, user interactions are integrated from two stages of user node embedding representations and topic inference. The topic detected by the method better pays attention to the correlation between the users, and higher continuity is obtained.

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 detection method based on message delivery and graph prior distribution. Background technique [0002] The rapid development of the Internet has brought great progress to our life. The popularity of social media allows each of us to have a platform to express our opinions and opinions. As a result, a large number of short texts are generated every day, and analyzing the topics in them is an important task, but manual analysis is time-consuming and laborious. The topic model can automatically extract document-topic distribution and topic-word distribution, helping people quickly analyze text and grasp text information. [0003] Traditional topic models, such as LDA, are widely used to discover potential topics from text corpora. Essentially, these methods reveal latent topics by implicitly capturing word co-occ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06F16/953G06N3/04G06N3/08
CPCG06Q50/01G06F16/953G06N3/088G06N3/047
Inventor 贺瑞芳王浩成刘焕宇
Owner TIANJIN UNIV
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