Artificial Intelligence Classification Model for Modern Chinese Poetry in Education

Mini Zhu, Gang Wang, Chaoping Li (), Hongjun Wang () and Bin Zhang
Additional contact information
Mini Zhu: College of Arts, Chongqing Three Gorges University, Chongqing 404020, China
Gang Wang: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Chaoping Li: College of Arts, Chongqing Three Gorges University, Chongqing 404020, China
Hongjun Wang: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Bin Zhang: Shanghai Film Academy, Shanghai University, Shanghai 200444, China

Sustainability, 2023, vol. 15, issue 6, 1-19

Abstract: Various modern Chinese poetry styles have influenced the development of new Chinese poetry; therefore, the classification of poetry style is very important for understanding these poems and promoting education regarding new Chinese poetry. For poetry learners, due to a lack of experience, it is difficult to accurately judge the style of poetry, which makes it difficult for learners to understand poetry. For poetry researchers, classification of poetry styles in modern poetry is mainly carried out by experts, and there are some disputes between them, which leads to the incorrect and subjective classification of modern poetry. To solve these problems in the classification of modern Chinese poetry, the eXtreme Gradient Boosting (XGBoost) algorithm is used in this paper to build an automatic classification model of modern Chinese poetry, which can automatically and objectively classify poetry. First, modern Chinese poetry is divided into words, and stopwords are removed. Then, Doc2Vec is used to obtain the vector of each poem. The classification model for modern Chinese poetry was iteratively trained using XGBoost, and each iteration promotes the optimization of the next generation of the model until the automatic classification model of modern Chinese poetry is obtained, which is named Modern Chinese Poetry based on XGBoost (XGBoost-MCP). Finally, the XGBoost-MCP model built in this paper was used in experiments on real datasets and compared with Support Vector Machine (SVM), Deep Neural Network (DNN), and Decision Tree (DT) models. The experimental results show that the XGBoost-MCP model performs above 90% in all data evaluations, is obviously superior to the other three algorithms, and has high accuracy and objectivity. Applying this to education can help learners and researchers better understand and study poetry.

Keywords: XGBoost; poetry style classification; Chinese modern poetry; education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/6/5265/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/6/5265/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:6:p:5265-:d:1098881

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().