Keyword extraction method and device, equipment and medium

Abstract

The invention discloses a keyword extraction method and device, equipment and a medium, and relates to the field of data processing. The method comprises the steps of obtaining a first comment text from a plurality of comment texts, wherein an emotion tag of the first comment text is a first emotion tag; performing word segmentation on the first comment text to obtain a feature word set of the first comment text; calculating an information entropy set of the first comment text according to the feature word set, wherein information entropies in the information entropy set are obtained through calculation according to feature words in the feature word set; and determining a keyword of the first comment text according to the information entropy set. According to the method and device, the information entropy set of the comment texts can be obtained, the keywords in the comment texts are determined according to the information entropy set, and the information entropy represents the difference between the comment texts, so that the keywords obtained through the information entropy have higher interpretability for the sentiment classification result, and the modeling effect and interpretability are improved.

Description

technical field

[0001] The present application relates to the field of data processing, in particular to a keyword extraction method, device, equipment and medium. Background technique

[0002] In actual business scenarios, in order to confirm the sentiment orientation of the user's comment text, sentiment classification will be performed on the comment text, for example, to determine whether the user comment text is an optimistic or pessimistic evaluation, after completing the sentiment classification. In order to improve interpretability, it is necessary to extract keywords under a certain sentiment classification.

[0003] The related technology is to input the comment text into the text sentiment classification model, output the sentiment label of the comment text, and classify the comment text according to the sentiment label, for example, classify the comment text into positive comment text and negative comment text. After the classification is completed, the technici...

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