@InProceedings{lin-EtAl:2017:I17-41,
  author    = {Lin, Zheng-Wen  and  Chang, Yung-Chun  and  Wang, Chen-Ann  and  Hsieh, Yu-Lun  and  Hsu, Wen-Lian},
  title     = {CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {95--99},
  abstract  = {Sentiment lexicon is very helpful in dimensional sentiment applications.
	Because of countless Chinese words, developing a method to predict unseen
	Chinese words is required. The proposed method can handle both words and
	phrases by using an ADVWeight List for word prediction, which in turn improves
	our performance at phrase level. The evaluation results demonstrate that our
	system is effective in dimensional sentiment analysis for Chinese phrases. The
	Mean Absolute Error (MAE) and Pearson's Correlation Coefficient (PCC) for
	Valence are 0.723 and 0.835, respectively, and those for Arousal are 0.914 and
	0.756, respectively.},
  url       = {http://www.aclweb.org/anthology/I17-4015}
}