@InProceedings{jiang-han:2017:WASSA2017,
  author    = {Jiang, Song  and  Han, Xiaotian},
  title     = {DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method},
  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     = {243--248},
  abstract  = {In this paper, we present a novel ensemble learning architecture for emotion
	intensity analysis, particularly a novel framework of ensemble method. The
	ensemble method has two stages and each stage includes several single machine
	learning models. In stage1, we employ both linear and nonlinear regression
	models to obtain a more diverse emotion intensity representation. In stage2, we
	use two regression models including linear regression and XGBoost. The result
	of stage1 serves as the input of stage2, so the two different type models
	(linear and non-linear) in stage2 can describe the input in two opposite
	aspects. We also added a method for analyzing and splitting multi-words
	hashtags and appending them to the emotion intensity corpus before feeding it
	to our model. Our model achieves 0.571 Pearson-measure for the average of four
	emotions.},
  url       = {http://www.aclweb.org/anthology/W17-5234}
}