Abstract
This study aims to compare gender-related words frequently appeared in news articles reflecting societal phenomena. For this analysis, we collected news articles in three sectors (economy, society, and entertainment) from the internet portal Naver providing news articles from various press media. The meaning of words used in online news articles is expressed in vector space through word2vec algorithm, and we compared gender-related words through word vector operation. As a result, we observed differences in gender-related words in three sectors. Our study can contribute to existing studies on gender differences and gender discrimination by identifying gender differences in gender-related words based on the large amount of online news articles.
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