Text topic processing method and device, electronic equipment and computer storage medium

Abstract

The embodiment of the invention relates to the technical field of computer data processing. The invention discloses a text topic processing method and device, electronic equipment and a computer storage medium. The method comprises the following steps: determining N K values included in a predetermined K value range according to a predetermined step length, subjecting the obtained first text dataset to topic clustering processing and text filtering processing in sequence through a K-means clustering algorithm based on the N K values, obtaining a text data set obtained after the Nth text filtering processing and contour coefficients corresponding to the N K values, wherein N is a positive integer not smaller than 2; determining a target K value from the N K values according to the contourcoefficients respectively corresponding to the N K values; and performing topic clustering processing on the text data set after the Nth text filtering processing through K-Means according to the determined target K value to obtain a second topic clustering result, and taking each topic included in the second topic clustering result as each topic of the first text data set.

Description

technical field [0001] The embodiments of the present application relate to the technical field of computer data processing, and specifically, the present application relates to a text topic processing method, device, electronic equipment, and computer storage medium. Background technique [0002] With the rapid development of the era of Internet big data, among the massive news information and information from multiple sources, hot topics in various fields can be quickly and automatically extracted, instead of artificially retrieving the current most concerned hot topics from the huge amount of information Information has become an inevitable trend of new media platforms. [0003] At present, hot topics are mainly extracted through text clustering. However, text clustering belongs to unsupervised learning, and the clustering speed is far slower than supervised learning. Even the most efficient clustering algorithm, its text clustering efficiency is very low, especially in ...

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Problems solved by technology

[0003] At present, hot topics are mainly extracted through text clustering. However, text clustering belongs to unsupervised learning, and the clustering speed is far slower than supervised learning. Even the most efficient clustering algorithm, its text clustering efficiency is very low, especially in In the face of massive text data, the clustering performance is even more unsatisfactory. Therefore, an efficient text clustering method is urgently needed to extract hot topics

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