Abstract
The style authors follow to express their ideas has been a subject of great debate. Several perspectives have been followed to try to analyze the style. In this contribution we present a computational methodology to study the writing style in a collection of hundreds of texts. For each text several attributes, which include different time series, are extracted and a battery of tools from the signal processing and the machine learning communities are applied to identify a set of features that may define a candidate style space. We applied self-organizing maps to visualize how several authors are distributed in the high-dimensional space associated to the style, and to visually prospect the similarities between styles from different authors.
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Neme, A., Hernández, S., Dey, T., Muñoz, A., Pulido, J.R.G. (2013). Computational Study of Stylistics: Visualizing the Writing Style with Self-Organizing Maps. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_27
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DOI: https://doi.org/10.1007/978-3-642-35230-0_27
Publisher Name: Springer, Berlin, Heidelberg
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