# puconghan/Computational-Journalism-for-People-s-Daily-Opinion

> Statistical topic models – one of the sub fields of machine learning and natural language processing – provide a data-driven framework for analyzing collections of text documents. It has become one of the most frequently used tools for computational journalism used to investigate abstract topics and keywords that occur in a collection of text documents. Digital journalists can use such tools to extract frequently appearing terms, and to analyze the trend of a particular news brand or stories about a social event. Articles, analyses and documents written in Chinese have become increasingly important for multimedia stories about China. Available Chinese archives on the Internet might contain stories that require digital journalists to apply appropriate topic modeling tools. Unlike English and other alphabetic languages, the basic structural unit of Chinese language is character encoded in Guobiao GB18030 or Unicode.  I implement apps using the Chinese topic-modeling tools jieba for computational journalism. This app analyzes articles from the opinion archive of the People’s Daily and generate a list of frequently appearing words using the keyword extraction tool provided by the jieba library.

- **Canonical URL**: https://githubrepo.cloud/repo/puconghan/Computational-Journalism-for-People-s-Daily-Opinion
- **GitHub Repository**: https://github.com/puconghan/Computational-Journalism-for-People-s-Daily-Opinion
- **Maintainer / Creator**: https://github.com/puconghan
- **Discovered On**: Sep 29, 2026

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