Interacting with Literary Style through Computational Tools

Style is an important aspect of writing, shaping how audi ences interpret t and engage with literary works. However, for most people style is difficult to articulate precisely. While users frequently interact with computational word processing tools with well- defined metrics, such as spelling and grammar checkers, style is a significantly more nuanced concept. In this paper, we present a computational technique to help surface sty le in written ext. We collect a data set of crowd source d human judgments of style, derive a model of style by training a neuralneton this data, and present novel applications for visu alizing and browsing style across broad bodies of literature, as well as anointer active text editor with real-time style feedback. We study these interactivity le applications with users and discuss implications for enabling this novel approach to style

M.S. Applied Data Science - Capstone Chronicles 2024

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