Public discourse on highly charged, complex social and political issues is extensive, with millions of sentences available for
analysis. It is also rife with metaphors that manifest vast numbers of novel metaphoric expressions. More and more, to understand
such issues, to see who is saying what and why, we require big data and statistically-based analysis of such corpora. However,
statistically-based data processing alone cannot do all the work. The MetaNet (MN) project has developed an analysis method that
formalizes existing insights about the conceptual metaphors underlying linguistic expressions into a computationally tractable
mechanism for automatically discovering new metaphoric expressions in texts. The ontology used for this computational method is
organized in terms of metaphor cascades, i.e. pre-existing packages of hierarchically organized primary and
general metaphors that occur together. The current paper describes the architecture of metaphor-to-metaphor relations built into
this system. MN’s methodology represents a proof of concept for a novel way of performing metaphor analysis. It does so by
applying the method to one particular domain of social interest, namely the gun debate in American political discourse. Though
well aware that such an approach cannot replace a thorough cognitive, sociological, and political analysis, this paper offers
examples that show how a cascade theory of metaphor and grammar helps automated data analysis in many ways.
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This list is based on CrossRef data as of 10 november 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
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