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. © 2013 Springer-Verlag.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 198 AISC, pp. 265–274). Springer Verlag. https://doi.org/10.1007/978-3-642-35230-0_27
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