Social network event detection method and device

Abstract

The invention provides a social network event detection method and device, and the method comprises the steps: taking a push text and a label extracted from a data set as nodes, and constructing a social network event heterogeneous graph; constructing a semantic view based on text contents of a push text and a label in the heterogeneous graph, and obtaining a feature matrix and a weighted adjacency matrix of the semantic view; constructing a time distribution view based on the push-texts in the heterogeneous graph and the time of the push-texts, and obtaining a feature matrix and a weighted adjacency matrix of the time distribution view; inputting the feature matrix and the weighted adjacency matrix of the semantic view , and the feature matrix and the weighted adjacency matrix of the timedistribution view into the GCN; and in combination with an attention mechanism, guiding feature fusion of the semantic view and the time distribution view by adopting a label, generating an attentiondistribution probability of the text semantic view and the time distribution view under a given label, obtaining a probability that each node in the heterogeneous graph belongs to an event in the data set, and realizing prediction of the nodes. According to the social network event detection method provided by the embodiment of the invention, the detection effect of the social network event is improved.

Description

technical field [0001] The present invention relates to the field of social network event detection, and more specifically, the present invention relates to a method and device for detecting social network events based on semantics and tags. Background technique [0002] Social network event detection is a hot research topic at present. Most traditional event detection methods are based on semantic analysis of message content, and they usually depend on text representation techniques, such as traditional topic models and sequence representations based on sentence deep neural networks, etc. The topic model method builds a graph model to mine the distribution relationship between text words and topics, and realizes the similarity comparison of texts. TwitterLDA is the first topic model proposed for short text semantic analysis for Tweet data. In recent years, with the rise of deep learning, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Graph Convolut...

AI Technical Summary

Problems solved by technology

Therefore, there are a large number of methods that combine multi-view data for feature representation, but they do not fully explore the potential semantic correlation between different views through consensus information.

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