@InProceedings{alam-EtAl:2016:PEOPLES,
  author    = {Alam, Firoj  and  Celli, Fabio  and  Stepanov, Evgeny A.  and  Ghosh, Arindam  and  Riccardi, Giuseppe},
  title     = {The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {143--152},
  abstract  = {In this paper, we address the issue of automatic prediction of readers’ mood
	from newspaper ar- ticles 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 cate- gories 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.},
  url       = {http://aclweb.org/anthology/W16-4316}
}