Research on Chinese Classification Based on TF-IDF

被引：0
|
作者：
Xiao, Liang [1 ]
Yao, Nianmin [1 ]
机构：
[1] Dalian Neusoft Univ Informat, Dalian 116023, Peoples R China
来源：
2021 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, INFORMATION AND COMMUNICATION ENGINEERING | 2021年 / 11933卷
关键词：
text classification; machine learning; TF-IDF; word segmentation;
D O I：
10.1117/12.2615301
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Chinese text classification has been in the research stage, there are many machine learning algorithms that can be used, such as logical regression, SVM, KNN, naive Bayes, random forest, neural network and so on. In this paper, taking Chinese modern novels as an example, we use various algorithms for classification and comparison, and choose the best algorithm for naive Bayes and neural network. After adjusting the TF-IDF algorithm and processing the participle according to the TF-IDF value, the accuracy of classification is improved obviously. The logistic regression with the lowest accuracy can increase about 6.7%,while the simple Bias and neural network can reach 100%.
引用
收藏
页数：6
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