Integrating Corpus Linguistics and Text Mining to Analyze European Media Coverage on China–EU Electric Vehicle Dispute

This study innovatively moves beyond traditional mono-method research by employing an integrated approach that synergizes corpus linguistics and text mining. Through sentiment, thematic, and collocational analyses, it critically examines the representation of China’s image in European media coverage of the China–EU electric vehicle dispute. Initially, sentiment analysis of news reports concerning EU tariffs on Chinese electric vehicles was conducted. Subsequently, four key themes emerged from analyzing a corpus consisting of 202 news articles: “market reaction,”; “trade war,” “China’s response,” and “dialogue and negotiation.” Finally, collocation analysis of the keywords “China” and “Beijing” reveals four main images of China in European media: China is framed as the unfair-subsidy provider, threatener, negotiator, and defender. The key conclusion is that European media coverage is characterized by discursive ambivalence, simultaneously portraying China as both a threat and a partner. These findings are significant as they illuminate how media discourse serves as a key arena where the economic and political complexities of the China–EU trade conflict are negotiated, legitimized, and managed.