Stylistics analysis and authorship attribution algorithms based on self-organizing maps

The style followed by authors can be thought of as a collection of attributes that defines the stylistics space. Texts from the same author tend to be similar in that space. However, the identification of stylistics spaces has proven to be challenging. Associated with the stylistics space is the authorship attribution task. On it, a text of unknown authorship is presented to a system, and the system is expected to identify the author of the text. Two modules define an authorship attribution algorithm: the stylistics space and a classifier. We present a methodology that includes both, a module that allows the identification of novel stylistics spaces, and a classifier to confront the authorship attribution task from the features that define space. The methodology imbricates feature selection, anomaly detection, classification, and visualization algorithms. We applied the capabilities of self-organizing maps not only for visualization but also for anomaly detection, which defines the basis of the classifier. We compared our authorship attribution algorithm with two existing ones. Our methodology achieved similar or better results under bag-of-words-related stylistics spaces, and it presented the lowest error under a novel stylistics space based on the rate of introduction of new words.