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Update to include new systems and resources that appeared on ACL2017

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@ -68,6 +68,8 @@ SemEval14-Task4: Annotated aspects and sentiments of laptops and restaurants rev
Stanford Sentiment Treebank: Sentiment dataset with fine-grained sentiment annotations [[web]](http://nlp.stanford.edu/sentiment/code.html) [[paper]](http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf)
Multidimensional Lexicon for Interpersonal Stancetaking [[web]](https://github.com/umashanthi-research/multidimensional-stance-lexicon) [[paper]](https://www.cc.gatech.edu/~jeisenst/papers/pavalanathan-acl-camera-ready.pdf)
## Open Source Implementations
The characteristics of each implementation are described.
@ -101,6 +103,9 @@ _**Caveats**: A key problem in sentiment analysis is its sensitivity to the doma
[openai/generating-reviews-discovering-sentiment](https://github.com/openai/generating-reviews-discovering-sentiment): Deep Learning, byte mLSTM [[paper]](https://arxiv.org/abs/1704.01444)
[yiyang-gt/social-attention](https://github.com/yiyang-gt/social-attention): Deep Learning, Attention-based. Uses authors'
position in the social network to aide sentiment analysis. [[paper]](https://arxiv.org/pdf/1511.06052.pdf).
### R
[timjurka/sentiment](https://github.com/timjurka/sentiment): Supervised Machine Learning, Naive Bayes Classifier.