现代信息科技2023，Vol.7Issue(23)：89-92,4.DOI:10.19850/j.cnki.2096-4706.2023.23.019
基于贝叶斯算法的中文新闻标题分类研究
Research on Chinese News Title Classification Based on Bayesian Algorithm
摘要
Abstract
Abstract is a high level summary of messages,therefore,how to effectively identify abstracts quickly and accurately is an important topic in the current field of Chinese abstract recognition.This paper proposes a news classification method that combines TF-IDF and Bayesian algorithm.Using the TF-IDF algorithm to extract the set of feature words in short text,captures the semantics expressed in the short text,and calculates the corresponding TF-IDF values.The TF-IDF values are formed into feature vectors as input to the Bayesian algorithm to achieve news text classification.Finally,the prediction results are evaluated based on the error rate.The experimental results indicate that this method can combine Bayesian method with TF-IDF to achieve rapid classification of information.关键词
贝叶斯算法/TF-IDF/新闻分类Key words
Bayesian algorithm/TF-IDF/news classification分类
信息技术与安全科学引用本文复制引用
郭泓,尚庆生,赵薇,韩运龙..基于贝叶斯算法的中文新闻标题分类研究[J].现代信息科技,2023,7(23):89-92,4.
