Research on Chinese classification based on TF-IDF

Liang Xiao; Nianmin Yao

doi:10.1117/12.2615301

15 October 2021 Research on Chinese classification based on TF-IDF

Liang Xiao, Nianmin Yao

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330B (2021) https://doi.org/10.1117/12.2615301
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

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%.

Citation Download Citation

Liang Xiao and Nianmin Yao "Research on Chinese classification based on TF-IDF", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330B (15 October 2021); https://doi.org/10.1117/12.2615301

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