Natural Language Processing of COVID-19 Reports Involving China in New York Times —a Machine-based Framing Study of Media Language

Natural Language Processing (NLP) is a most promising and powerful method for big data analysis. It is gaining increasing attention from language researchers with its potentiality in information extraction, automatic indexing, textual framing, topic modeling, sensitivity analysis and other machine analytics studies. Through employing the LDA topic modeling and NLTK (Natural Language Toolkit) Vader SentimentAnalyser, this research makes a contrastive study of the overall news coverage in New York Times (NYT) against the backdrop of Covid-19 and its China-specific reports, with the aim of addressing what areas of concern were respectively selected and foregrounded to the public in these two types, what sensitivities were revealed and how linguistic devices were used to frame China's response to Covid-19. Analysis of metaphorical expressions in NYT shows that metaphors tended to be employed as a device to realize the dominant negative polarity latent in the reports and thus establish unfavourable images of China. This study deepens the methodological endeavors in media and linguistic studies through combining content analysis and machine-based analysis.