A text classification system based on text vectorization

By using text vectoring based on text classification system to construct and calculate text feature vectors, the problem of low accuracy of text classification in the prior art is solved, and more efficient and accurate text classification is achieved.

CN118779454BActive Publication Date: 2025-03-21BEIJING SHANGSHI TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202410989064.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-07-23
Publication Date
2025-03-21
Estimated Expiration
2044-07-23

AI Technical Summary

Technical Problem

In the prior art, text classification accuracy is low, making it difficult to adapt to the diversity and flexibility of language expression.

Method used

The text classification system based on text vectorization is adopted, including information acquisition module, keyword extraction module, type vector construction module, text vector construction module, text classification module, exception analysis module and machine learning module, and the accuracy of text classification is improved by constructing and calculating text feature vectors.

Benefits of technology

By improving the accuracy and efficiency of keyword extraction, feature vector construction and similarity calculation, the accuracy and efficiency of text classification are significantly improved, and the problem of low accuracy of text classification in the prior art is solved.

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Abstract

The present invention relates to the technical field of text topic classification, and in particular to a text classification system based on text vectorization, the system comprising an information acquisition module for acquiring texts of various topic types and texts to be classified; a keyword extraction module for extracting topic type keywords from preprocessed texts of various topic types; a type vector construction module for constructing feature vectors of texts of various topic types; a text vector construction module for constructing feature vectors of various topic types of texts to be classified; a text classification module for classifying texts into topic types; an abnormality analysis module for analyzing the abnormality of classification of texts to be classified; and a machine learning module for optimizing the topic type classification process of texts to be classified in the next monitoring week according to the analysis results of the abnormality of classification of texts to be classified within the monitoring period and the number of texts to be classified. The present invention improves the accuracy of text classification.
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