@inproceedings{di-buono-etal-2017-predicting,
    title = "Predicting News Values from Headline Text and Emotions",
    author = "di Buono, Maria Pia  and
      {\v{S}}najder, Jan  and
      Dalbelo Ba{\v{s}}i{\'c}, Bojana  and
      Glava{\v{s}}, Goran  and
      Tutek, Martin  and
      Milic-Frayling, Natasa",
    editor = "Popescu, Octavian  and
      Strapparava, Carlo",
    booktitle = "Proceedings of the 2017 {EMNLP} Workshop: Natural Language Processing meets Journalism",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4201",
    doi = "10.18653/v1/W17-4201",
    pages = "1--6",
    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.",
}