@InProceedings{dahou-EtAl:2016:COLING,
  author    = {Dahou, Abdelghani  and  Xiong, Shengwu  and  Zhou, Junwei  and  Haddoud, Mohamed Houcine  and  Duan, Pengfei},
  title     = {Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2418--2427},
  abstract  = {With the development and the advancement of social networks, forums, blogs and
	online sales, a growing number of Arabs are expressing their opinions on the
	web. In this paper, a scheme of Arabic sentiment classification, which
	evaluates and detects the sentiment polarity from Arabic reviews and Arabic
	social media, is studied. We investigated in several architectures to build a
	quality neural word embeddings using a 3.4 billion words corpus from a
	collected 10 billion words web-crawled corpus. Moreover, a convolutional neural
	network trained on top of pre-trained Arabic word embeddings is used for
	sentiment classification to evaluate the quality of these word embeddings. The
	simulation results show that the proposed scheme outperforms the existed
	methods on 4 out of 5 balanced and unbalanced datasets.},
  url       = {http://aclweb.org/anthology/C16-1228}
}