  Books have the power to make us feel happiness, sadness, pain, surprise, or
sorrow. An author's dexterity in the use of these emotions captivates readers
and makes it difficult for them to put the book down. In this paper, we model
the flow of emotions over a book using recurrent neural networks and quantify
its usefulness in predicting success in books. We obtained the best weighted
F1-score of 69% for predicting books' success in a multitask setting
(simultaneously predicting success and genre of books).
