@inproceedings{alam-etal-2016-social,
title = "The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems",
author = "Alam, Firoj and
Celli, Fabio and
Stepanov, Evgeny A. and
Ghosh, Arindam and
Riccardi, Giuseppe",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara",
booktitle = "Proceedings of the Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media ({PEOPLES})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4316",
pages = "143--152",
abstract = "In this paper, we address the issue of automatic prediction of readers{'} mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.",
}
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<abstract>In this paper, we address the issue of automatic prediction of readers’ mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.</abstract>
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%0 Conference Proceedings
%T The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems
%A Alam, Firoj
%A Celli, Fabio
%A Stepanov, Evgeny A.
%A Ghosh, Arindam
%A Riccardi, Giuseppe
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F alam-etal-2016-social
%X In this paper, we address the issue of automatic prediction of readers’ mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.
%U https://aclanthology.org/W16-4316
%P 143-152
Markdown (Informal)
[The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems](https://aclanthology.org/W16-4316) (Alam et al., PEOPLES 2016)
ACL