Training sample generation, text data, public opinion event classification method and related equipment

Abstract

Training sample generation is disclosed, Text data, A method for classify public opinion events and relate equipment, in a training sample generation method provided by an embodiment of the present application, At first, that text data is cluster, Because text data is clustered, When a clustering result corresponding to a target category is found, The training samples of the target category can beobtained only by selecting the text data that meet the target category conditions in the corresponding clustering results and then labeling the target category, without analyzing whether the text data in other clusters meet the target category conditions or not. Therefore, the selection range of text data is greatly narrowed, the efficiency of annotation and the accuracy of samples are improved,and the time of annotation text data is shortened. At the same time, it improves the efficiency and accuracy of text data classification and public opinion event classification process.

Description

technical field [0001] The present application relates to the field of Internet technology, and more specifically, to a method for generating training samples, text data, public opinion event classification and related equipment. Background technique [0002] In recent years, with the rapid development of the Internet, network media has become a new form of information dissemination. Netizens' active speech has reached an unprecedented level. Whether it is a major domestic or international event, online public opinion can be formed immediately. If the content of public opinion is not effectively monitored and managed, it is likely to cause negative social effects. [0003] The core of public opinion monitoring is to capture the information of concern from the complicated information on the Internet according to certain rules and methods, classify the captured information, and use the classified information to identify the information represented by this type of information....

AI Technical Summary

Problems solved by technology

[0005] It can be seen from this that the more accurate the classification result is, the more data needs to be labeled. Therefore, in order to improve the accuracy of the classification, a lot of data needs to be labeled, which takes a lot of time in the implementation process.

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