A Computer-Aided Affective Content Analysis of Nanotechnology Newspaper Articles

Abstract

This paper explores the application of an affective content analysis to a selection of nanotechnology news articles gathered from selected newspapers. Thematic content analyses dominate current efforts to mine large text collections of popular science media; the addition of an affective analysis element can yield useful information to supplement future content analysis efforts. Using Whissell’s Dictionary of Affect in Language, the analysis rates news articles gathered over a twenty-two year period for their pleasantness, activeness, and imagery, determining the mean affective tone in each category for the entire collection, four ‘content themes’ (business, national security, health, and environment), and the change in affective tone over the twenty-two year period these articles represent. Whereas the entire collection analyzed as a whole rates very similarly to the average found for everyday English language use and the ‘content themes’ show similar results, the change in affective tone over the years has been both significant and striking. A sample of results from three years of the twenty-two year period is then qualitatively explored to demonstrate to the reader the connection between the quantitative results of the Dictionary and the qualitative effect of the article’s word use and phrasing. The paper ends with a review of the technique’s success, implications for policymaking and public dialogue, and avenues for future use.