@InProceedings{balikas:2017:SemEval,
  author    = {Balikas, Georgios},
  title     = {TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification},
  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     = {755--759},
  abstract  = {The paper describes the participation of the team ``TwiSE'' in the SemEval-2017
	challenge.
	Specifically, I participated at Task 4 entitled ``Sentiment Analysis in
	Twitter'' for which I implemented systems for five-point tweet classification
	(Subtask C) and five-point tweet quantification (Subtask E) for English tweets.
	In the feature extraction steps the systems rely on the vector space model,
	morpho-syntactic analysis of the tweets and several sentiment lexicons. 
	The classification step of Subtask C uses a Logistic Regression trained with
	the one-versus-rest approach. Another instance of Logistic Regression combined
	with the classify-and-count approach is trained for the quantification task of
	Subtask E.  
	In the official leaderboard the system is ranked \textit{5/15} in Subtask C and
	\textit{2/12} in Subtask E.},
  url       = {http://www.aclweb.org/anthology/S17-2127}
}