{'fraction_non_alphanumeric': 0.04697072838665759, 'fraction_numerical': 0.019741320626276378, 'mean_word_length': 5.228813559322034, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A CORPUS-BASED STUDY OF LEXICAL COLLOCATIONS OF KEYWORDS FOUND IN ONLINE BUSINESS NEWS ARTICLES

Business news articles are crucial for Business English students since they provide a rich source for learning vocabulary in their field of study and professional communication. This study investigates lexico-grammatical items specific to a specialised corpus of online business news reports. Its overall aim is twofold: 1) to generate a keywords list extracted from the self-built corpus; and 2) to identify lexical collocations of the first ten keywords. Seven hundred online business news articles from BBC news.com were compiled to build a corpus of business news. The data were analysed by the application of Antconc 3.5.9 developed by Anthony (2020). The results derived from the present study through a corpus-based analysis uncovered a list of keywords which were frequently used in online business news where the majority of the keywords were nouns (63.51%), verbs/nouns (8%), adjectives (7%), adjectives/nouns (4%), adverbs (2%), and verb (1%). From the selected top one hundred keywords, the lexical collocations of each keyword were identified with 3 combination types based on the set framework adapted from Benson et al.’s (2010). The obtained combination types include Adjective+Noun (40%), Noun+Noun (35%), and Noun+Verb (20%), respectively. The pedagogical implications and recommendations for future research were also discussed.  Article visualizations: