Income, Nationality and Subjectivity in Media Text

Abstract

This article takes a bird’s eye view of how positive or negative sentiments in the news press about countries and nationality nouns seem to reflect the country’s general income groups. The study focuses on the four income groups classified by the World Bank and their co-occurrence with positively and negatively classified adjectives from the Subjectivity Lexicon for Czech. A search in the journalistic subcorpus of the SYN series, release 8 of the Czech National Corpus, results in a time line covering three decades. Previous research on subjectivity has either focused on other parts of the Subjectivity Lexicon or on fewer adjectives from other languages. In this article, it is argued that the income groups are treated in descending order, i.e., the higher the income, the more positive the sentiment. Even when the most influential groups in the top and bottom are removed, the result holds. Discourse concerning global war and peace, and the security of different nations, is also detected as a result.

When is a Crisis Really a Crisis? Using NLP and Corpus Linguistic Methods to Reveal Differences in Migration Discourse Across Czech Media

“…In the Czech context, most studies either use qualitative methods (Průchová Hrůzová 2021) or small data sets from limited outlets (Kluknavská 2021), with an overreliance on manual content analysis (Esser et al 2019). Using computational methods, such as clustering (Urbániková -Tkaczyk 2020) or corpusassisted discourse analysis (Elmerot 2021(Elmerot , 2022, is rare. Natural language processing (NLP) tools have gained popularity in interdisciplinary studies, with landmark Törnbergs' (2016) combination of topic modeling and discourse analysis, and some Corpus Linguistics (CL) scholars advocate the utility of adding close reading to reveal the meaning behind automated results (Brookes -McEnery, 2019).…”