  The ability to automatically determine the age audience of a novel provides
many opportunities for the development of information retrieval tools. Firstly,
developers of book recommendation systems and electronic libraries may be
interested in filtering texts by the age of the most likely readers. Further,
parents may want to select literature for children. Finally, it will be useful
for writers and publishers to determine which features influence whether the
texts are suitable for children. In this article, we compare the empirical
effectiveness of various types of linguistic features for the task of age-based
classification of fiction texts. For this purpose, we collected a text corpus
of book previews labeled with one of two categories -- children's or adult. We
evaluated the following types of features: readability indices, sentiment,
lexical, grammatical and general features, and publishing attributes. The
results obtained show that the features describing the text at the document
level can significantly increase the quality of machine learning models.
