일본 IT뉴스 어휘 분석 연구

Abstract

In this work, we analyze one-month articles about IT in the field of economy extracted from Sankei News. We utilize the tool KH Coder in order to analyze the vocabulary of the articles in terms of parts-of-speech and kinds of words and organize the result in the order of the frequency of word types. This process reveals that the number of word types is 1,964, which enables us to determine the preference order whereby learners study words in the order of frequently attested words. As for the classification of words based on parts-of-speech, the nouns account for 73.3%, the verbs 14.1%, the adjectives 5.9%, the adverbs 5.7%, the interjections 0.2%, the negative auxiliaries 0.2%, and the unknown words 0.2%. As for the classification of words based on kinds of word, the number of target words is 1,716 out of 1,964 word types. The kinds of words in this study consist of native Japanese words, Chinese loan words, non-Chinese loan words, and hybrid words, with native Japanese words being further subdivided into hiragana-words and kanji-words. Chinese loan words being set side, non-Chinese loan words are divided into katakana-words and Romanized-words. Hybrid words are divided into “native Japanese words+Chinese loan words,” “Chinese loan words+native Japanese words,” and “non-Chinese loan words + Chinese loan words.”