@InProceedings{le-EtAl:2016:ALR12,
  author    = {Le, Tuan Anh  and  Moeljadi, David  and  Miura, Yasuhide  and  Ohkuma, Tomoko},
  title     = {Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets},
  booktitle = {Proceedings of the 12th Workshop on Asian Language Resources (ALR12)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {123--131},
  abstract  = {This paper describes our attempt to build a sentiment analysis system for
	Indonesian tweets. With this system, we can study and identify sentiments and
	opinions in a text or document computationally. We used four thousand manually
	labeled tweets collected in February and March 2016 to build the model. Because
	of the variety of content in tweets, we analyze tweets into eight groups in
	total, including pos(itive), neg(ative), and neu(tral). Finally, we obtained
	73.2% accuracy with Long Short Term Memory (LSTM) without normalizer.},
  url       = {http://aclweb.org/anthology/W16-5415}
}