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        <title>Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets</title>
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    <name type="personal">
        <namePart type="given">Tomoko</namePart>
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            <title>Proceedings of the 12th Workshop on Asian Language Resources (ALR12)</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets
%A Le, Tuan Anh
%A Moeljadi, David
%A Miura, Yasuhide
%A Ohkuma, Tomoko
%Y Hasida, Koiti
%Y Wong, Kam-Fai
%Y Calzorari, Nicoletta
%Y Choi, Key-Sun
%S Proceedings of the 12th Workshop on Asian Language Resources (ALR12)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F le-etal-2016-sentiment
%X 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.
%U https://aclanthology.org/W16-5415
%P 123-131

Markdown (Informal)

[Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets](https://aclanthology.org/W16-5415) (Le et al., ALR 2016)

• Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets (Le et al., ALR 2016)

ACL

• Tuan Anh Le, David Moeljadi, Yasuhide Miura, and Tomoko Ohkuma. 2016. Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets. In Proceedings of the 12th Workshop on Asian Language Resources (ALR12), pages 123–131, Osaka, Japan. The COLING 2016 Organizing Committee.