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Event evolution analysis method of short text data

An event evolution, short text technology, applied in the field of event evolution analysis of short text data, can solve the problem of not being able to track the event evolution process dynamically in real time, and not suitable for short text data.

Active Publication Date: 2013-06-12
INST OF COMPUTING TECHNOLOGY - CHINESE ACAD OF SCI
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  • Application Information

AI Technical Summary

Problems solved by technology

However, tools such as GAC-INCR only analyze the data statically, and cannot track the evolution process of events dynamically in real time.
In addition, the clustering method used by GAC-INCR is not suitable for short text data

Method used

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  • Event evolution analysis method of short text data
  • Event evolution analysis method of short text data
  • Event evolution analysis method of short text data

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Embodiment Construction

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, a short text data event evolution analysis method proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0043] According to an embodiment of the present invention, a method for analyzing event evolution of short text data is provided. Specifically include the following steps:

[0044] The first step is to obtain the events of the first period and the number of articles associated with each event.

[0045] First, in the initial period, according to the thesaurus with a fixed size of D (that is, including D terms), and N short text data (or called N documents / articles) input in real time during the initial period, the first The document-term matrix of th...

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Abstract

The invention provides an event evolution analysis method of short text data, which comprises the steps of constructing a document-lexical item matrix of the current time period according to a lexicon and the short text data input in the current time period, conducting non-negative matrix factorization on the document-lexical item matrix, obtaining a document-event matrix and an event-lexical item matrix, calculating similarity between an event of the current time period and an event of the previous time period according to the event-lexical item matrix, constructing an event relation graph of the current time period according to the similarity, the event of the current time period and a residual graph of the previous time period, dividing the event relation graph of the current time period into one or more subgraphs, classifying the subgraphs to obtain a newly generated event set and an evolution event set, calculating a document amount associated with each event according to the document-event matrix, and analyzing and predicting a trend of the evolution event set according to the document amount to serve as a constraint condition of the non-negative matrix factorization of the next time period. The method is suitable for tracking an event evolution process of the short text data dynamically.

Description

technical field [0001] The invention relates to the field of data mining, in particular to an event evolution analysis method for short text data. Background technique [0002] With the emergence of web2.0 technology, users are more and more involved in existing network applications. Among them, Weibo is a very popular type of network application at present, and it is an information sharing, dissemination and acquisition platform based on user relationships. Users can transmit information, comment, etc. through short text data on Weibo. How to deal with these short text data has attracted more and more attention. In the data mining process of short text data, there are usually three requirements: first, discover newly generated topics (or events, topics) from short text data in a timely manner; tracking; third, create system models that can withstand large-scale network data. [0003] However, data mining for short text data such as Weibo is quite difficult. The reasons a...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
Inventor 程学旗刘盛华李福鑫王元卓刘悦
Owner INST OF COMPUTING TECHNOLOGY - CHINESE ACAD OF SCI
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