@InProceedings{chen-huang-chen:2017:SemEval,
  author    = {Chen, Chung-Chi  and  Huang, Hen-Hsen  and  Chen, Hsin-Hsi},
  title     = {NLG301 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {847--851},
  abstract  = {Short length, multi-targets, target relation-ship, monetary expressions, and
	outside reference are characteristics of financial tweets. This paper proposes
	methods to extract target spans from a tweet and its referencing web page.
	Total 15 publicly available sentiment dictionaries and one sentiment dictionary
	constructed from training set, containing sentiment scores in binary or real
	numbers, are used to compute the sentiment scores of text spans. Moreover, the
	correlation coeffi-cients of the price return between any two stocks are
	learned with the price data from Bloomberg. They are used to capture the
	relationships between the interesting tar-get and other stocks mentioned in a
	tweet. The best result of our method in both sub-task are 56.68% and 55.43%,
	evaluated by evaluation method 2.},
  url       = {http://www.aclweb.org/anthology/S17-2144}
}