  With the widespread use of the internet, the size of the text data increases
day by day. Poems can be given as an example of the growing text. In this
study, we aim to classify poetry according to poet. Firstly, data set
consisting of three different poetry of poets written in English have been
constructed. Then, text categorization techniques are implemented on it.
Chi-Square technique are used for feature selection. In addition, five
different classification algorithms are tried. These algorithms are Sequential
minimal optimization, Naive Bayes, C4.5 decision tree, Random Forest and
k-nearest neighbors. Although each classifier showed very different results,
over the 70% classification success rate was taken by sequential minimal
optimization technique.
