@InProceedings{agrawal-an:2016:COLING,
  author    = {Agrawal, Ameeta  and  An, Aijun},
  title     = {Selective Co-occurrences for Word-Emotion Association},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1579--1590},
  abstract  = {Emotion classification from text typically requires some degree of word-emotion
	association, either gathered from pre-existing emotion lexicons or calculated
	using some measure of semantic relatedness. Most emotion lexicons contain a
	fixed number of emotion categories and provide a rather limited coverage.
	Current measures of computing semantic relatedness, on the other hand, do not
	adapt well to the specific task of word-emotion association and therefore,
	yield average results. In this work, we propose an unsupervised method of
	learning word-emotion association from large text corpora, called Selective
	Co-occurrences (SECO), by leveraging the property of mutual exclusivity
	generally exhibited by emotions. Extensive evaluation, using just one seed word
	per emotion category, indicates the effectiveness of the proposed approach over
	three emotion lexicons and two state-of-the-art models of word embeddings on
	three datasets from different domains.},
  url       = {http://aclweb.org/anthology/C16-1149}
}