A Text Mining Analysis of US-Chinese Leaders on Trade Policy

Abstract

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and China on the issues of trade were collected and analytically examined for US-China gaps in major foreign policies, such as bilateral trade and the Belt and Road Initiative. In this paper, a term frequency-inverse document frequency word cloud, a network similarities index, machine learning-processed latent Dirichlet allocation (LDA), and structural equivalence are applied to examine the meanings of the speeches. The main arguments in this paper are as follows. First, the document similarity between the speeches of Chinese and US leaders appears to be completely different. Also, while the documents from Chinese leaders are considerably similar, the documents from US leaders differ by far. Secondly, LDA topic analysis indicates that China concentrates more on international and collaborative relationships, while the U.S. has more focus on domestic and economic interests. Third, from a word hierarchy analysis, the basic words used by American and Chinese leaders are also completely different. Agriculture, farmers, automobiles, and negotiations are the basic words for American leaders, but for Chinese leaders, the basic words are planning, markets, and education.

An evolutionary analysis of new energy and industry policy tools in China based on large-scale policy topic modeling

“…Policy documents are often studied by text analysis approaches. Korean nuclear policy texts released in 2003 to 2016 are investigated with LSA [ 23 ], publications and articles regarding Russia’s renewable energy are categorized with big data analysis [ 24 ], official speeches on trade policies are examined by statistical text mining and LDA method [ 25 ]. Massive abstracts of papers on neighborhood sustainability are studied and discussed by clustering methods at both geographical and temporal levels [ 26 ].…”