Text topic mining method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides a text topic mining method and device, computer equipment and a storage medium. The method comprises the steps: employing a Gaussian kernel function to calculate and obtain a similar matrix based on a plurality of texts; performing spectral clustering on the plurality of texts based on the similar matrix to obtain a plurality of text clusters; extracting theme keywords of each text cluster; calculating the reading frequency of each text in each text cluster, and calculating the reading frequency of the theme keyword of the corresponding text cluster based on the reading frequency of each text; and mining according to the reading times of the topic keyword of each text cluster to obtain a text topic. According to the method, the problem of theme dispersion can be solved, and the mined themes better meet the actual requirements of users.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a text topic mining method, device, computer equipment and storage medium. Background technique [0002] In the education and training scenario, user generated content (UGC) is one of the important means of agent course production, which has a positive effect on increasing course supply and improving training effects. [0003] The inventor found in the process of realizing the present invention that there are problems such as scattered content and uneven quality levels in the current UGC creation. Then, by extracting text keywords, such as TF-IDF, LDA, etc. to extract text topics or keywords, The extracted topics will be scattered and not concentrated. Therefore, it is difficult to really form a reference for topic selection, and it is impossible to actually form business application value. Contents of the invention [0004] In view of the above, it is ...

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Problems solved by technology

[0003] The inventor found in the process of realizing the present invention that there are problems such as scattered content and uneven quality levels in the current UGC creation. Then, by extracting text keywords, such as TF-IDF, LDA, etc. to extract text topics or keywords, The extracted topics will be scattered and not concentrated. Therefore, it is difficult to really form a reference for topic selection, and it is impossible to actually form business application value

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