Abstract
Authorship attribution has attracted the attention of the natural language processing and machine learning communities in the past few years. Here we are interested in finding a general measure of the style followed in the texts from the three main candidates in the Mexican presidential elections of 2012. We analyzed dozens of texts (discourses) from the three authors. We applied tools from the time series processing field and machine learning community in order to identify the overall attributes that define the writing style of the three authors. Several attributes and time series were extracted from each text. A novel methodology, based in mutual information, was applied on those time series and attributes to explore the relevance of each attribute to linearly separate the texts accordingly to their authorship. We show that less than 20 variables are enough to identify, by means of a linear recognizer, the authorship of a text from within one of the three considered authors.
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Neme, A., Hernández, S., Carrión, V. (2013). Identification of the Minimal Set of Attributes That Maximizes the Information towards the Author of a Political Discourse: The Case of the Candidates in the Mexican Presidential Elections. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_8
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DOI: https://doi.org/10.1007/978-3-642-37798-3_8
Publisher Name: Springer, Berlin, Heidelberg
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