@inproceedings{le-etal-2016-sentiment,
    title = "Sentiment Analysis for Low Resource Languages: A Study on Informal {I}ndonesian Tweets",
    author = "Le, Tuan Anh  and
      Moeljadi, David  and
      Miura, Yasuhide  and
      Ohkuma, Tomoko",
    editor = "Hasida, Koiti  and
      Wong, Kam-Fai  and
      Calzorari, Nicoletta  and
      Choi, Key-Sun",
    booktitle = "Proceedings of the 12th Workshop on {A}sian Language Resources ({ALR}12)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/fix-sig-urls/W16-5415/",
    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."
}