@InProceedings{madisetty-desarkar:2017:WASSA2017,
  author    = {Madisetty, Sreekanth  and  Desarkar, Maunendra Sankar},
  title     = {NSEmo at EmoInt-2017: An Ensemble to Predict Emotion Intensity in Tweets},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {219--224},
  abstract  = {In this paper, we describe a method to predict emotion intensity in tweets. 
	Our approach is an ensemble of three regression methods. The first method uses
	content-based features (hashtags, emoticons, elongated words, etc.). The second
	method considers word n-grams and character n-grams for training. The final
	method uses lexicons, word embeddings, word n-grams, character n-grams for
	training the model. An ensemble of these three methods gives better performance
	than individual methods. We applied our method on WASSA emotion dataset.
	Achieved results are as follows: average Pearson correlation is 0.706, average
	Spearman correlation is 0.696, average Pearson correlation for gold scores in
	range 0.5 to 1 is 0.539, and average Spearman correlation for gold scores in
	range 0.5 to 1 is 0.514.},
  url       = {http://www.aclweb.org/anthology/W17-5230}
}