Author
Listed:
- Ruikai Yuan
- Si Shen
- Yu Zheng
Abstract
Literature has vital historical values that reveal the truth of culture, ideology of people, political focus, and human experiences like a series of no-photo films via a combination of flat words. As the carrier of history, literature plays like one decent clerk who recorded the historical presentation of modern China through a proliferation of scholarly articles. This study aims to provide a comprehensive overview of the emerging focus of modern Chinese literary research with the Latent Dirichlet Allocation (LDA) topic model to explore the research themes and trends of former researchers from 1927 to 2023. LDA is a text-mining-based approach utilized to reveal principal themes from a substantial dataset of short textual documents. A total of 14,148 articles published between 1927 and October 2023 were collected and analyzed. Findings suggest that the primary scholarly focus areas of CNKI have been significantly influenced by historical events and foreign cultures, notably the Soviet Union and America. Over the past decades, the research trend has also shifted from revolution, Chinese ambient culture, and Soviet literature to American literature, gender, digital tools, and global issues. The primary research themes and new trends identified by LDA are instrumental in aiding researchers in discerning contemporary research questions and making more informed decisions. The findings of this research could be utilized in complementarily exploring the ideology of Chinese people in different periods.
Suggested Citation
Ruikai Yuan & Si Shen & Yu Zheng, 2025.
"Emerging Focus in Chinese Literary Study (1927 to 2023): Latent Dirichlet Allocation (LDA) Based Topic Modeling Analysis,"
SAGE Open, , vol. 15(3), pages 21582440251, August.
Handle:
RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251365788
DOI: 10.1177/21582440251365788
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:sagope:v:15:y:2025:i:3:p:21582440251365788. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.