@InProceedings{sedoc-preoctiucpietro-ungar:2017:EACLshort,
  author    = {Sedoc, Joao  and  Preo\c{t}iuc-Pietro, Daniel  and  Ungar, Lyle},
  title     = {Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {564--571},
  abstract  = {Inferring the emotional content of words is important for text-based sentiment
	analysis, dialogue systems and psycholinguistics, but word ratings are
	expensive to collect at scale and across languages or domains. We develop a
	method that automatically extends word-level ratings to unrated words using
	signed clustering of vector space word representations along with affect
	ratings. We use our method to determine a word's valence and arousal, which
	determine its position on the circumplex model of affect, the most popular
	dimensional model of emotion. Our method achieves superior out-of-sample word
	rating prediction on both affective dimensions across three different languages
	when compared to state-of-the-art word similarity based methods. Our method can
	assist building word ratings for new languages and improve downstream tasks
	such as sentiment analysis and emotion detection.},
  url       = {http://www.aclweb.org/anthology/E17-2090}
}