Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books

Suraj Maharjan, Sudipta Kar, Manuel Montes, Fabio A. González, Thamar Solorio


Abstract
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).
Anthology ID:
N18-2042
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
259–265
Language:
URL:
https://www.aclweb.org/anthology/N18-2042
DOI:
10.18653/v1/N18-2042
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-2042.pdf