@InProceedings{li-EtAl:2017:SemEval1,
  author    = {Li, Quanzhi  and  Nourbakhsh, Armineh  and  Liu, Xiaomo  and  Fang, Rui  and  Shah, Sameena},
  title     = {funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts},
  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     = {741--746},
  abstract  = {This paper describes the approach we used for SemEval-2017 Task 4: Sentiment
	Analysis in Twitter. Topic-based (target-dependent) sentiment analysis has
	become attractive and been used in some applications recently, but it is still
	a challenging research task. In our approach, we take the left and right
	context of a target into consideration when generating polarity classification
	features.  We use two types of word embeddings in our classifiers: the general
	word embeddings learned from 200 million tweets, and sentiment-specific word
	embeddings learned from 10 million tweets using distance supervision.  We also
	incorporate a text feature model in our algorithm. This model produces features
	based on text negation, tf.idf weighting scheme, and a Rocchio text
	classification method. We participated in four subtasks (B, C, D \& E for
	English), all of which are about topic-based message polarity classification.
	Our team is ranked \#6 in subtask B, \#3 by MAEu and \#9 by MAEm in subtask C, \#3
	using RAE and \#6 using KLD in subtask D, and \#3 in subtask E.},
  url       = {http://www.aclweb.org/anthology/S17-2125}
}