@InProceedings{zhong-wang:2017:I17-4,
  author    = {Zhong, Peng  and  Wang, Jingbin},
  title     = {LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {84--88},
  abstract  = {Sentiment analysis on Chinese text has intensively studied. The basic task for
	related research is to construct an affective lexicon and thereby predict
	emotional scores of different levels. However, finite lexicon resources make it
	difficult to effectively and automatically distinguish between various types of
	sentiment information in Chinese texts. This IJCNLP2017-Task2 competition seeks
	to automatically calculate Valence and Arousal ratings within the hierarchies
	of vocabulary and phrases in Chinese. We introduce a regression methodology to
	automatically recognize continuous emotional values, and incorporate a word
	embedding technique. In our system, the MAE predictive values of Valence and
	Arousal were 0.811 and 0.996, respectively, for the sentiment dimension
	prediction of words in Chinese. In phrase prediction, the corresponding results
	were 0.822 and 0.489, ranking sixth among all teams.},
  url       = {http://www.aclweb.org/anthology/I17-4013}
}