Computational opposition analysis using word embeddings: A method for strategising resonant informal argument

In informal argument, an essential step is to ask what will “resonate” with a particular audience and hence persuade. Marketers, for example, may recommend a certain colour for a new soda can because it “pops” on Instagram; politicians may “fine-tune” diction for different social demographics. This paper engages the need to strategise for such resonance by offering a method for automating opposition analysis (OA), a technique from semiotics used in marketing and literary analysis to plot objects of interest on oppositional axes. Central to our computational approach is a reframing of texts as proxies for thought and opposition as the product of oscillation in thought in response to those proxies, a model to which the contextual similarity information contained in word embeddings is relevant. We illustrate our approach with an analysis of texts on gun control from ProCon.org, implementing a three-step method to: 1) identify relatively prominent signifiers; 2) rank possible opposition pairs on prominence and contextual similarity scores; and 3) derive plot values for proxies on opposition pair axes. The results are discussed in terms of strategies for informal argument that might be derived by those on each side of gun control.

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