Minsk, Belarus
English-language environmental speech has its own linguistic features. Ecological discourse is a relevant, self-sufficient, and highly identical communication continuum. This article introduces a corpus analysis of the English-language environmental discourse with its linguistic patterns and functional interpretation. The texts were considered not as static linguistic objects, but as a system of functional features in speech. The English-language research material was authentic and featured various environmental issues. Corpus analysis is a popular and reliable tool for big linguistic data while the corpus-discursive methodology proved to be an effective interdisciplinary tool. It was applied to the EuroNews Green Corpus, aggregated from 506 English-language texts on environmental issues. This innovative type of corpus can be qualified as instrumentally independent. The research proved the effectiveness of universal case managers or case shells of open architecture, e.g., Sketch Engine. Not only did it manage to collect the data, but also performed quite a complex structure analysis, interpreting, and discourse modeling. Corpus linguistics is looking for qualitatively new opportunities to identify complex linguistic relations. The case study provided a comprehensive description of environmental discourse as an object of linguistic research. Based on statistic details, the authors analyzed the categories of tokens, keywords, and terms to structure them as lemmas, collocations, and n-grams, which were presented as metadata. The metadata modeling provided concordances, thesauruses, and frequency glossaries. Environmental discourse had a pronounced lexical and semantic uniqueness resulting from specific ecological terminology. However, the multidimensional corpus analysis revealed an extremely low frequency of the key concept of ecology. Thus, this corpus study of the English-language ecological discourse demonstrated a good potential for further linguistic research.
corpus research, ecological discourse, instrument-independent corpus, linguistic regularities, functional features, statistics, metadata
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