Incorporating text mining into critical discourse studies : a corpus-assisted discourse study of press representations of climate change in China

Abstract

This study integrates text mining with Critical Discourse Studies (CDS) to conduct a corpus-assisted discourse study of climate change representations in China Daily over the period from 2001 to 2020. Utilising the text mining tool KH Coder, the study examines the corpus from various perspectives, including co-occurrence network analysis, correspondence analysis, and the analysis of words co-occurring with China and the US. Methodologically, the chapter posits that the fusion of text mining and CDS offers a multifaceted approach to data processing at different textual levels, thereby yielding insights that are unattainable through traditional corpus linguistic methods. Empirically, the findings reveal that China Daily, as an official English-language newspaper, has always aligned with the Chinese government's evolving climate change policies. While it represented climate change as a general issue before 2009, it tended to feature a positive representation of China and a negative representation of the US as the Chinese government began to take a more active role in addressing climate change issues after 2009.