Cataloged from PDF version of article.This work is partially supported by the Scientific and Technical Research Council of Turkey (TÜBİTAK) under the grant number 109E006.Authorship attribution and identifying time period of literary works are fundamental problems
in quantitative analysis of languages. We investigate two fundamentally different machine learning text
categorization methods, Support Vector Machines (SVM) and Naïve Bayes (NB), and several style
markers in the categorization of Ottoman poems according to their poets and time periods. We use the
collected works (divans) of ten different Ottoman poets: two poets from each of the five different
hundred-year periods ranging from the 15th to 19 th century. Our experimental evaluation and statistical
assessments show that it is possible to obtain highly accurate and reliable classifications and to
distinguish the methods and style markers in terms of their effectiveness
