A corpus-based study of lexical collocations of keywords found in online news articles

This corpus-based study investigates lexical collocations of keywords found in online news articles, aiming to equip high school students in Thailand with the linguistic tools necessary for English admission exams. The primary objectives of this investigation are twofold: 1) to compile a list of 100 keywords essential for exam preparation from the self-constructed corpus; and 2) to identify the lexical collocations involving the first ten keywords. The study categorizes these keywords and collocations, by using 350 online news articles from CNN. The corpus was developed by aggregating online news articles, which were analyzed using AntConc software. The findings reveal a keyword distribution where nouns are predominant (57%), alongside verbs, adjectives, and adverbs, reflecting the dynamic nature of news language. From the top one hundred keywords, lexical collocations for each were meticulously identified, categorized into combination types such as Noun+Noun, Noun+Verb, Verb+Noun, and Adjective+Noun. These results not only enrich the vocabulary learning for students but also guide educators in designing more targeted teaching materials. This study concludes with pedagogical implications and suggestions for further research in the field of English language education, emphasizing the critical role of authentic textual analysis in preparing students for successful academic and professional futures.