Research on text classification technology based on natural language processing

Dandan Song

doi:10.1117/12.2653546

20 October 2022 Research on text classification technology based on natural language processing

Dandan Song

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123500Y (2022) https://doi.org/10.1117/12.2653546
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

Natural Language Processing (NLP) is a science that integrates computer knowledge, mathematical knowledge and linguistic knowledge, and text classification and recognition is considered to be an important research field and direction of natural language processing. This paper mainly studies the realization of text classification model through the processing method of text data in natural language processing and the theoretical knowledge and technical means of machine learning. We summarize the existing text classification algorithms, analyze their applicable scenarios, and optimize on the basis of these algorithm models. The paper proposes a Chinese text classification algorithm based on weight preprocessing. Algorithm based on the weight preprocessing link, so that the optimized classifier model can improve the accuracy of the existing text classifier. In this paper, the English Newsgroups corpus is used for experimental verification. The experimental results show that the classification accuracy and accuracy of the improved algorithm are better than those of the traditional text classification algorithm, thereby improving the accuracy of English text classification.

Citation Download Citation

Dandan Song "Research on text classification technology based on natural language processing", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123500Y (20 October 2022); https://doi.org/10.1117/12.2653546

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available