@InProceedings{medhaffar-EtAl:2017:W17-13,
  author    = {Medhaffar, Salima  and  Bougares, Fethi  and  Est\`{e}ve, Yannick  and  Hadrich-Belguith, Lamia},
  title     = {Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {55--61},
  abstract  = {Dialectal Arabic (DA) is significantly different from the Arabic
	language taught in schools and used in written communication and formal speech
	(broadcast news, religion, politics, etc.). There are many existing researches
	in the field of Arabic language Sentiment Analysis (SA); however, they are
	generally restricted to Modern Standard Arabic (MSA) or some dialects of
	economic or political interest. In this paper we are interested in the SA of
	the Tunisian Dialect. We utilize Machine Learning techniques to determine the
	polarity of comments written in Tunisian Dialect. First, we evaluate the SA
	systems performances with models trained using freely available MSA and
	Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect
	corpus of 17.000 comments from Facebook. This corpus allows us a significant
	accuracy improvement compared to the best model trained on other Arabic
	dialects or MSA data.
	We believe that this first freely available corpus will be valuable to
	researchers working in the field of Tunisian Sentiment Analysis and similar
	areas.},
  url       = {http://www.aclweb.org/anthology/W17-1307}
}