Detection method of network sudden hot events based on topic model

Abstract

The invention discloses a network burst hotspot event detection method based on a topic model, which comprises the following steps of: 1, firstly, carrying out participle treatment on a file data set to obtain a word list, a file word relation matrix, a word file distribution matrix and a word date distribution matrix; 2, screening the data set according to relevant words in an emerging process of network hotspot events and burst characteristics of a file; 3, obtaining characteristic words and characteristic texts of the burst hotspot events through topic modeling; and 4, figuring out attention date distribution of the hotspot events. Compared with the prior art, the invention has the advantages that the topic modeling is carried out by using the topic model, thus a topic event can be more accurately described; and a burst characteristic computing method of words is introduced and then the data set is screened, thus time-unrelated topics are removed through filtering, and an actual burst hotspot event is obtained.

Description

Technical field [0001] The invention relates to the field of topic models and event detection, and in particular to a method for detecting network hotspot events based on topic models. Background technique [0002] With the rapid development and wide application of network technology, the Internet has gradually become an important channel for people to obtain information. There are hundreds of millions of network information emerging worldwide every day. How to detect sudden hot events in massive network information has become An emerging research topic. [0003] Traditional topic models, such as PLSA (Probabilistic Latent Semantic Analysis), LDA (Latent Dirichlet Allocation), etc., can be used to perform topic mining on a document set. They approximate each topic in the document set through iterative calculations. However, these topic models are based on the BOW (Bag Of Words) model, which only considers the affiliation of words and documents, ignoring the time information of wor...

AI Technical Summary

Problems solved by technology

[0005] Although this method can detect certain network emergencies, there are still some defects: 1) clustering is not the best topic modeling method, and topic model is more suitable for topic mining; 2) When generating candidate topics, this method does not optimize the original data according to the burst characteristics of time series data, resulting in more time-independent static topics in candidate topics

On the other hand, the theme of emergencies we care about will also be weakened by the interference of static themes

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