Network hot topic discovery method and system

Abstract

The present invention proposes a network hot topic discovery method and system based on sparse matrix decomposition and a topic model. The method comprises: constructing a word co-occurrence matrix for web texts, for massive web texts, removing low-frequency lexical terms when the document size reaches a certain level, and using the words to construct the word co-occurrence matrix; carrying out sparse non-negative matrix decomposition, carrying out decomposition on the word co-occurrence matrix to obtain a lexical term-topic matrix and a document topic matrix, and taking the matrices as the next input; and taking the two matrices obtained by matrix decomposition as the initial condition to input into the topic model, obtaining the optimal solution through the expectation maximization algorithm, and carrying out steps such as sorting the number of documents with specific topics, and the like to finally realize the discovery of network hot topics.

Description

technical field [0001] The invention relates to a method and system for discovering network hot topics, which adopts a method combining sparse matrix decomposition and topic model, and belongs to the field of artificial intelligence. Background technique [0002] Hot topics on the Internet not only greatly affect the formation and development of various events in the virtual society, but also affect people's views and judgments on events in reality. In order to grasp hot topics and changes in public opinion in a timely manner through massive social network data, it is necessary to extract, track and predict topics, which also makes hot topics from the Internet a research hotspot in recent years. Traditional methods such as Guo Lantian, Li Yang, Mu Dejun and others proposed similarity calculation and clustering of text topic words based on clustering rules, which are limited by manual formulation of clustering rules, and the classification effect on specific text topics is li...

AI Technical Summary

Problems solved by technology

Traditional methods such as Guo Lantian, Li Yang, Mu Dejun and others proposed similarity calculation and clustering of text topic words based on clustering rules, which are limited by manual formulation of clustering rules, and the classification effect on specific text topics is limited. Good, but hardly universal

At the same time, due to the characteristics of short length, less information, and high-dimensional sparse network data, it brings great difficulties to the hot topic discovery task.

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