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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Juola, P.: Authorship attribution. NOW Press (2008)
Stamatatos, E.: A survey of modern authorship attribution methods. J. of the American Soc. for Information Science and Technology 60(3), 538–556 (2010)
Canter, D.: An evaluation of Cusum stylistics analysis of confessions. Expert Evidence 1(2), 93–99 (1992)
Garrard, P., Maloney, L.M., Hodges, J.R., Patterson, K.: The effects of very early Alzheimer’s disease on the characteristics of writing by a renowned author. Brain 128, 250–260 (2005)
Mayer, R., Rauber, A.: On Wires and Cables: Content Analysis of WikiLeaks Using Self-Organising Maps, pp. 238–246 (2011)
Neme, A., Cervera, A., Lugo, T.: Authorship attribution as a case of anomaly detection: A neural network model. Int. J. of Hybrid Intell. Syst. 8, 225–235 (2011)
Manning, C., Schutze, H.: Foundations of statistical natural language processig. MIT Press (2003)
Lagus, K., Kaski, S., Kohonen, T.: Mining massive document collections by the WEBSOM method. Information Sciences 163(1-3), 135–156 (2004)
Abarbanel, H.: Analysis of observed chaotic data. Springer (1996)
Kantz, H., Schreiber, T.: Nonlinear time series analysis, 2nd edn. Cambridge Press
Cellucci, C.J., Albano, A.M., College, B., Rapp, P.E.: Statistical Validation of Mutual Information Calculations: Comparison of Alternative Numerical Algorithms. Physical Review E 71(6) (2005), doi:10.1103/PhysRevE.71.066208
Shannon, C.E.: A Mathematical Theory of Communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)
Kohonen, T.: Self-organizing maps, 2nd edn. Springer (2000)
Hujun, Y.: The Self-Organizing Maps: Background, Theories, Extensions and Applications. Studies in Computational Intelligence (SCI) 115, 715–762 (2008)
Quinlan, R.: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)
Cortes, M.L., Ruiz-Shulcloper, J., Alba-Cabrera, E.: An overview of the evolution of the concept of testor. Pattern Recognition 34, 753–762 (2001)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. of Machine Learning Res. 3, 1157–1182 (2003)
Hernández, S., Neme, A.: Identification of the minimal set of attributes that maximizes the authorship information (to appear in LNCS, 2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-35230-0_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35229-4
Online ISBN: 978-3-642-35230-0
eBook Packages: EngineeringEngineering (R0)