@InProceedings{htait-fournier-bellot:2017:SemEval,
  author    = {Htait, Amal  and  Fournier, S\'{e}bastien  and  Bellot, Patrice},
  title     = {LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification},
  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     = {718--722},
  abstract  = {We present, in this paper, our contribution in SemEval2017 task 4 :
	”Sentiment Analysis in Twitter”, subtask A: ”Message Polarity
	Classification”, for
	English and Arabic languages. Our system is based on a list of sentiment seed
	words
	adapted for tweets. The sentiment relations between seed words and other terms
	are captured by cosine similarity between the word embedding representations
	(word2vec). These seed words are extracted from datasets of annotated tweets
	available online. Our tests, using these seed words, show significant
	improvement in results compared to the use of Turney and Littman’s (2003)
	seed words, on polarity classification of tweet messages.},
  url       = {http://www.aclweb.org/anthology/S17-2120}
}