@InProceedings{wu-EtAl:2017:I17-4,
  author    = {Wu, Chuhan  and  Wu, Fangzhao  and  Huang, Yongfeng  and  Wu, Sixing  and  Yuan, Zhigang},
  title     = {THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {47--52},
  abstract  = {Predicting valence-arousal ratings for words and phrases is very useful for
	constructing affective resources for dimensional sentiment analysis. Since the
	existing valence-arousal resources of Chinese are mainly in word-level and
	there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for
	Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for
	Chinese affective words and phrases automatically. In this task, we propose an
	approach using a densely connected LSTM network and word features to identify
	dimensional sentiment on valence and arousal for words and phrases jointly. We
	use word embedding as major feature and choose part of speech (POS) and word
	clusters as additional features to train the dense LSTM network. The evaluation
	results of our submissions (1st and 2nd in average performance) validate the
	effectiveness of our system to predict valence and arousal dimensions for
	Chinese words and phrases.},
  url       = {http://www.aclweb.org/anthology/I17-4007}
}