Automatic Authorship Attribution for Texts in Croatian Language Using Combinations of Features

Abstract

In this work we investigate the use of various character, lexical, and syntactic level features and their combinations in automatic authorship attribution. Since the majority of text representation features are language specific, we examine their application on texts written in Croatian language. Our work differs from the similar work in at least three aspects. Firstly, we use slightly different set of features than previously proposed. Secondly, we use four different data sets and compare the same features across those data sets to draw stronger conclusions. The data sets that we use consist of articles, blogs, books, and forum posts written in Croatian language. Finally, we employ a classification method based on a strong classifier. We use the Support Vector Machines to learn classifiers which achieve excellent results for longer texts: 91% accuracy and F1 measure for blogs, 93% acc. and F1 for articles, and 99% acc. and F1 for books. Experiments conducted on forum posts show that more complex features need to be employed for shorter texts.