Abstract
Most automatic sentiment analyses of texts tend to only employ a simple positive-negative polarity to classify emotions. In this paper, I illustrate a more fine-grained automatic sentiment analysis [Jockers, Matthew. 2016. Introduction to the Syuzhet package. https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhetvignette.html (accessed 07 March 2017).; Mohammad, Saif M. & Peter D. Turney. 2013. Crowd sourcing a word-emotion association lexicon. Computational Intelligence 29(3). 436–465.] that is based on a classification of human emotions that has been put forward by psychological research [Plutchik, Robert. 1994. The psychology and biology of emotion. New York, NY: HarperCollins College Publishers.]. The advantages of this approach are illustrated by a sample study that analyses the emotional sentiment of the campaign speeches of the two main candidates of the 2016 US presidential election.
Author supplied keywords
Cite
CITATION STYLE
Hoffmann, T. (2018). “Too many Americans are trapped in fear, violence and poverty”: A psychology-informed sentiment analysis of campaign speeches from the 2016 US presidential election. Linguistics Vanguard, 4(1). https://doi.org/10.1515/lingvan-2017-0008
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.