@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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="di-buono-etal-2017-predicting">
<titleInfo>
<title>Predicting News Values from Headline Text and Emotions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="given">Pia</namePart>
<namePart type="family">di Buono</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jan</namePart>
<namePart type="family">Šnajder</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bojana</namePart>
<namePart type="family">Dalbelo Bašić</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Goran</namePart>
<namePart type="family">Glavaš</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Martin</namePart>
<namePart type="family">Tutek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natasa</namePart>
<namePart type="family">Milic-Frayling</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism</title>
</titleInfo>
<name type="personal">
<namePart type="given">Octavian</namePart>
<namePart type="family">Popescu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carlo</namePart>
<namePart type="family">Strapparava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Copenhagen, Denmark</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">di-buono-etal-2017-predicting</identifier>
<identifier type="doi">10.18653/v1/W17-4201</identifier>
<location>
<url>https://aclanthology.org/W17-4201</url>
</location>
<part>
<date>2017-09</date>
<extent unit="page">
<start>1</start>
<end>6</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Predicting News Values from Headline Text and Emotions
%A di Buono, Maria Pia
%A Šnajder, Jan
%A Dalbelo Bašić, Bojana
%A Glavaš, Goran
%A Tutek, Martin
%A Milic-Frayling, Natasa
%Y Popescu, Octavian
%Y Strapparava, Carlo
%S Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F di-buono-etal-2017-predicting
%X 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.
%R 10.18653/v1/W17-4201
%U https://aclanthology.org/W17-4201
%U https://doi.org/10.18653/v1/W17-4201
%P 1-6
Markdown (Informal)
[Predicting News Values from Headline Text and Emotions](https://aclanthology.org/W17-4201) (di Buono et al., 2017)
ACL
- Maria Pia di Buono, Jan Šnajder, Bojana Dalbelo Bašić, Goran Glavaš, Martin Tutek, and Natasa Milic-Frayling. 2017. Predicting News Values from Headline Text and Emotions. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, pages 1–6, Copenhagen, Denmark. Association for Computational Linguistics.