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. Since there are no spaces between words in Chinese documents, topic-modeling tools for analyzing English text documents, such as gensim library, cannot be fully applied to analyze Chinese text documents. The gensim library can neither separate Chinese characters into segments nor convert Chinese characters to vectors using the bag-of-words approach. It only accepts pre-compiled documents with Chinese word segmentations with UTF-8 encoding. Digital journalists can take advantage of gensim statistical analyses, such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and Term Frequency–Inverse Document Frequency (TF-IDF) to analyze existing corpus created by other tools. In this paper, I will analyze one of the Chinese topic-modeling tools jieba and its application to computational journalism. I will analyze 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. I will also apply gensim TF-IDF analysis to existing Chinese segmentation documents represented as bag-of-words counts and apply a weighting, which discounts common terms. I will compare the jieba keyword extraction results with the gensim TF-IDF results. The People’s Daily is one of the most influential state-owned presses in China. The opinion section of the newspaper provides direct information on the policies and viewpoints of the Party. By analyzing the two lists of frequently appearing words, I want to predict the viewpoint of the government and its emphasis during the period of leadership transition. By analyzing the two lists of words, each from the same month, but in a different year, I want to learn whether the voice of the Party repeat in the same month of each year.