@InProceedings{he-EtAl:2017:WASSA2017,
  author    = {He, Yuanye  and  Yu, Liang-Chih  and  Lai, K. Robert  and  Liu, Weiyi},
  title     = {YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model},
  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     = {238--242},
  abstract  = {The EmoInt-2017 task aims to determine a continuous numerical value
	representing the intensity to which an emotion is expressed in a tweet.
	Compared to classification tasks that identify 1 among n emotions for a tweet,
	the present task can provide more fine-grained (real-valued) sentiment
	analysis. This paper presents a system that uses a bi-directional LSTM-CNN
	model to complete the competition task. Combining bi-directional LSTM and CNN,
	the prediction process considers both global information in a tweet and local
	important information. The proposed method ranked sixth among twenty-one teams
	in terms of Pearson Correlation Coefficient.},
  url       = {http://www.aclweb.org/anthology/W17-5233}
}