Topic modeling and evolutionary trends of China's language policy: A LDA-ARIMA approach.

BACKGROUND

Language policy serves as an essential tool for governments to guide and regulate language development. However, China's current language policy faces challenges like outdated analytical methods, inefficiencies caused by policy misalignment, and the absence of predictive frameworks. This study provides a comprehensive overview of China's language policy by identifying key topics and predicting future trends.

METHODS

We employ the Latent Dirichlet Allocation topic model and Autoregressive Integrated Moving Average model systematically analyze and predict the evolution of China's language policy. By gathering a large-scale textual data of 1,420 policy texts from 2001-2023 on official websites, we achieve both topic extraction and evolution prediction.

RESULTS

This study reveals that: (1) Language life, language education, and language resources have high popularity indexes, and language education and language planning exhibit high expected values. (2) The theme intensity of most topics has been a significant upward trend since 2014, with significant fluctuations during T1-T2. (3) From 2001 to 2023, the actual and fitted values show an overall positive trend. In 2024-2028, the predicted value of language resources stabilizes after a brief decline in 2024, while other topics show upward trends.

CONCLUSIONS

This study extracts 1,420 policy texts from official websites and outlines the following findings: (1) Language policies focus on maintaining a harmonious linguistic environment, addressing educational inequality, and protecting language resources. (2) Since 2014, most topics have exhibited fluctuating yet sustained growth trend, particularly in language education and research. (3) Except for language resources, the predicted values of the remaining six topics will show a growing trend from 2024 to 2028. Based on these findings, we propose policy recommendations such as strengthening language research, developing a multilingual education system, and optimizing language resource management.