Identification of changes in literary writing style using machine learning

Abstract

This research aims to identify changes in the writing style over time of 7 authors of Englishspeaking novels. For each author, an organization of the novels was carried out according to the date of publication. The novels were classified in three stages called initial, intermediate and final; each stage contains 3 novels. Between two consecutive stages there are at least 2 years of separation between the publication dates of the novels. To solve the problem of detecting changes in writing style over time, it is proposed to use a supervised automatic learning-based approach. Vector space models were created from the frequencies of use of n-grams of different types and lengths. In addition, the algorithm of Principal Component Analysis (PCA) was used as the n-gram selection method. The solution was addressed as a classification problem using the Vector Support Machine algorithms (Support Vector Machine, SVM), Naive Bayes Multinomial (Multinomial Naive Bayes, MNB), Logistic Regression (LG) and Liblinear as classifiers. The metric to measure the efficiency of the learning algorithms was accuracy. The research showed significant changes in five of the authors with an average accuracy between 70% and 80% in the different types of n-grams.