Visualizing Incongruity and Resolution: Visual Data Mining Strategies for Modeling Sequential Humor Containing Shifts of Interpretation
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The goal of this paper is to investigate the use of visualization as an approach to modeling humor within text. In particular, we developed algorithmic and automated approaches to visualizing and detecting shifts in interpretation as intelligent agents parse meaning from garden path jokes. Garden path jokes can occur when a reader’s initial interpretation of an ambiguous text turns out to be incorrect, leading them down the wrong path to a semantic dead end. Given new information, semantic incongruities arise that require resolution, often triggering a humorous response. This is a work of visual text mining, that is visualizing texts in order to detect patterns and features associated with various text based phenomena such as humor. In this paper we describe three successful approaches to text visualization conducive to identifying distinguishing features given humorous and non humorous texts. These are the use of paired collocated coordinates, heat maps, and two-dimensional Boolean plots. The proposed methodology and tools offer a new approach to testing and generating hypotheses related to theories of humor as well as other phenomena involving incongruity-resolution and shifts in interpretation including non-verbal humor.
Smigaj, A., & Kovalerchuk, B. (2017). Visualizing Incongruity and Resolution: Visual Data Mining Strategies for Modeling Sequential Humor Containing Shifts of Interpretation. DAPI 2017: Distributed, Ambient and Pervasive Interactions, 660–674. https://doi.org/10.1007/978-3-319-58697-7_49
DAPI 2017: Distributed, Ambient and Pervasive Interactions
© Springer International Publishing AG 2017