In this thesis, we explore the classical works by famous authors available in Project Gutenberg – a free online ebook library. The contemporary computational power enables us to analyze thousands of books and find similarities between them. We explore the differences between books and genres with respect to features such as proportion of stop words, the distribution of part of speech classes or frequencies of individual words. Using this knowledge, we create a model which predicts book metadata, including author or genre, and compare the performance of different approaches. With multinomial naive Bayes model, we reached 74.1 % accuracy on the author prediction task out of more than 1 400 authors. For other metadata, the random forest classifier achieved the best results. Through most predictive features, we try to capture what is typical for individual genres or epochs. As a part of the analysis, we create Character Interactions model that enables us to visualize the interactions between characters in the book and define the main or central character of the book.

@mastersthesis{CLUMLMB16,
	author	 = {Jan Bilek},
	title	 = {{Characterizing Literature Using Machine Learning Methods}},
	advisors	 = {Julian Kunkel},
	year	 = {2016},
	month	 = {10},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:jan_bilek_characterizing_literature_using_machine_learning_methods.pdf}}},
	type	 = {Master's Thesis},
	abstract	 = {In this thesis, we explore the classical works by famous authors available in Project
			Gutenberg – a free online ebook library. The contemporary computational power enables us to
			analyze thousands of books and find similarities between them. We explore the differences
			between books and genres with respect to features such as proportion of stop words, the
			distribution of part of speech classes or frequencies of individual words. Using this
			knowledge, we create a model which predicts book metadata, including author or genre, and
			compare the performance of different approaches. With multinomial naive Bayes model, we
			reached 74.1 \% accuracy on the author prediction task out of more than 1 400 authors. For
			other metadata, the random forest classifier achieved the best results. Through most
			predictive features, we try to capture what is typical for individual genres or epochs. As a
			part of the analysis, we create Character Interactions model that enables us to visualize the
			interactions between characters in the book and define the main or central character of the
			book.},
}