Machine Learning Based News Text Classification

Abstract

With the rapid development of the Internet era, the explosive growth of news data volume and the lack of effective management are gradually becoming serious problems, and it is increasingly difficult for readers to obtain valuable information quickly. How to quickly search for valuable information from the large amount of news text information is a meaningful task in text classification. Existing research methods still have some problems, such as directly combining the headline with the text, thus neglecting the importance of the headline, and the single model of classification, which leads to low classification results. For this reason, the main objective of this paper is to investigate the classification of news texts based on machine learning. This paper examines the current state of deep learning-based text classification and, in combination with the characteristics of news texts, chooses a machine learning-based text classification method to further explore and study news texts. Through the response time and core algorithm accuracy test of this system, the system better reflects the excellent performance of the system and meets the actual performance requirements of the system. The proposed news system can not only process system requests quickly, but also has excellent accuracy rate, which can better assist users to filter information and improve the user's experience of reading news.