Co-word Analysis using the Chinese Character Set

TL;DR

This paper demonstrates how co-word analysis can be effectively applied to Chinese texts by utilizing character-based segmentation and semantic mapping, revealing meaningful patterns in scientific journal titles.

Contribution

It introduces a method for co-word analysis of Chinese texts using character segmentation and semantic maps, addressing previous language-specific challenges.

Findings

Successful visualization of word occurrence matrix

Factor analysis reveals underlying semantic structures

Method enhances readability of semantic maps in Chinese texts

Abstract

Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic maps. Chinese characters contain not only information, but also meaning. This may enhance the readability of semantic maps. In this study, we analyze 58 words which occur ten or more times in the 1652 journal titles of the China Scientific and Technical Papers and Citations Database. The word occurrence matrix is visualized and factor-analyzed.