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@ -106,6 +106,8 @@ _**Caveats**: A key problem in sentiment analysis is its sensitivity to the doma
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[yiyang-gt/social-attention](https://github.com/yiyang-gt/social-attention): Deep Learning, Attention-based. Uses authors'
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[yiyang-gt/social-attention](https://github.com/yiyang-gt/social-attention): Deep Learning, Attention-based. Uses authors'
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position in the social network to aide sentiment analysis. [[paper]](https://arxiv.org/pdf/1511.06052.pdf).
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position in the social network to aide sentiment analysis. [[paper]](https://arxiv.org/pdf/1511.06052.pdf).
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[thunlp/NSC](https://github.com/thunlp/NSC): Deep Learning, Attention-based. Uses user and production information.[[paper]](http://anthology.aclweb.org/D/D16/D16-1171.pdf).
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### R
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### R
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[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.
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[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.
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