An analysis of affective expressions in articles of popular science by text mining

This paper aims to analyze affective expressions in articles of popular science by text mining with the keywords “Cancer” and “Immunity”. This study selects 145 articles from the website of a magazine and segmented them into 410,919 terms. And the study uses an automatic system to classify the terms into vocabulary categories, selecting the affective terms with specific vocabulary categories. The results show those the affective terms in the analyzed articles of popular science are not significantly differential with year and decade. But there is a significantly negative correlation with the quantity of articles that are published in the same year. That is, the more articles are published the less proportion of affective terms to the summary terms occurs on the articles.