@inproceedings{8853763b549d4fe8acaf3d85a73daba2,
title = "Predicting news values from headline text and emotions",
abstract = "We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models - an SVM and a CNN - to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information.",
author = "{Di Buono}, {Maria Pia} and Jan {\v S}najder and Ba{\v s}ic, {Bojana Dalbelo} and Goran Glava{\v s} and Martin Tutek and Natasa Milic-Frayling",
note = "Publisher Copyright: {\textcopyright} EMNLP 2017.All right reserved.; EMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017 ; Conference date: 07-09-2017",
year = "2017",
language = "English",
series = "EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "1--6",
editor = "Octavian Popescu and Carlo Strapparava",
booktitle = "EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop",
address = "United States",
}