@InProceedings{abdulmageed:2017:W17-13,
  author    = {Abdul-Mageed, Muhammad},
  title     = {Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {147--156},
  abstract  = {Although there is by now a considerable amount of research on subjectivity and
	sentiment analysis on morphologically-rich languages, it is still unclear how
	lexical information can best be modeled in these languages. To bridge this gap,
	we build effective models exploiting exclusively gold- and machine-segmented
	lexical input and successfully employ syntactically motivated feature selection
	to improve classification. Our best models achieve significantly above the
	baselines, with 67.93% and 69.37% accuracies for subjectivity and sentiment
	classification respectively.},
  url       = {http://www.aclweb.org/anthology/W17-1318}
}