@inproceedings{1,
	title={Sentiment analysis of Twitter data for predicting stock market movements},
	author={Pagolu, Venkata Sasank and Reddy, Kamal Nayan and Panda, Ganapati and Majhi, Babita},
	booktitle={2016 international conference on signal processing, communication, power and embedded system (SCOPES)},
	pages={1345--1350},
	year={2016},
	organization={IEEE},
	url = {https://arxiv.org/pdf/1610.09225.pdf}
}

@inproceedings{2,
	title={Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators},
	author={Indera, NI and Yassin, IM and Zabidi, A and Rizman, ZI},
	booktitle={Journal of Fundamental and Applied Sciences. Vol.35, No.35},
	pages={791--808},
	year={2017},
	organization={University of El Oued},
	url = {https://www.ajol.info/index.php/jfas/article/viewFile/165614/155073}
}

@inproceedings{3,
	title={Predicting Bitcoin price fluctuation with Twitter sentiment analysis},
	author={Evita Stenqvist, Jacob Lonno},
	booktitle={},
	pages={},
	year={2017},
	organization={Diva},
	url = {http://www.diva-portal.org/smash/get/diva2:1110776/FULLTEXT01.pdf}
}

@inproceedings{4,
	title={Predict Tomorrows Bitcoin (BTC) Price with Recurrent Neural Networks},
	author={Orhan Gazi Yalcin},
	booktitle={},
	pages={},
	year={2018},
	organization={Towards Data Science},
	url = {https://towardsdatascience.com/using-recurrent-neural-networks-to-predict-bitcoin-btc-prices-c4ff70f9f3e4}
}

@inproceedings{5,
	title={Stock Predictions through News Sentiment Analysis},
	author={Intel-Corporation},
	booktitle={},
	pages={},
	year={2017},
	organization={Code Project},
	url = {https://www.codeproject.com/Articles/1201444/Stock-Predictions-through-News-Sentiment-Analysis}
}

@inproceedings{6,
	title={Predicting the Price of Bitcoin Using Machine Learning},
	author={Sean McNally, Jason Roche, Simon Caton},
	booktitle={2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)},
	pages={344--347},
	year={2018},
	organization={IEEE},
	url = {https://ieeexplore.ieee.org/abstract/document/8374483}
}

@inproceedings{7,
	title={Search Tweets},
	author={Twitter},
	booktitle={},
	pages={},
	year={2018},
	organization={Twitter Developers},
	url = {https://developer.twitter.com/en/docs/tweets/search/overview}
}

@inproceedings{8,
	title={Consuming streaming data},
	author={Twitter},
	booktitle={},
	pages={},
	year={2018},
	organization={Twitter Developers},
	url = {https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data.html}
}

@inproceedings{9,
	title={Streaming With Tweepy},
	author={Joshua Roesslein},
	booktitle={},
	pages={},
	year={2009},
	organization={Tweepy},
	url = {http://docs.tweepy.org/en/v3.4.0/streaming_how_to.html}
}

@inproceedings{10,
	title={Using Linked Data for polarity classification of patients experiences},
	author={Mehrnoush Shamsfard, Samira Noferesti},
	booktitle={Journal of Biomedical Informatics},
	pages={6-19},
	year={2015},
	organization={Elsevier},
	url = {https://www.sciencedirect.com/science/article/pii/S1532046415001276}
}

@inproceedings{11,
	title={Social media sentiment analysis: lexicon versus machine learning},
	author={Chedia Dhaoui, Cynthia M. Webster, Lay Peng Tan},
	booktitle={Journal of Consumer Marketing, Volume 34. Issue 6},
	pages={},
	year={2017},
	organization={Emerald Insight},
	url = {https://www.emeraldinsight.com/doi/pdfplus/10.1108/JCM-03-2017-2141}
}

@inproceedings{12,
	title={VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text},
	author={C.J. Hutto and Eric Gilbert},
	booktitle={Eighth International Conference on Weblogs and Social Media (ICWSM-14)},
	pages={},
	year={2014},
	organization={Ann Arbor, MI},
	url = {https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/download/8109/8122}
}

@inproceedings{13,
	title={Wisdom of Crowds},
	author={Will Kenton},
	booktitle={},
	pages={},
	year={2018},
	organization={Investopedia},
	url = {https://www.investopedia.com/terms/w/wisdom-crowds.asp}
}

@inproceedings{14,
	title={A Beginner's Guide to Neural Networks and Deep Learning},
	author={Skymind},
	booktitle={A.I. Wiki},
	pages={},
	year={2018},
	organization={Skymind},
	url = {https://skymind.ai/wiki/neural-network}
}

@inproceedings{15,
	title={What is a neural network},
	author={Jonas DeMuro},
	booktitle={World of tech},
	pages={},
	year={2018},
	organization={techradar},
	url = {https://www.techradar.com/uk/news/what-is-a-neural-network}
}

@inproceedings{16,
	title={Supervised dictionary learning},
	author={Mairal, J., Ponce, J., Sapiro, G., Zisserman, A. and Bach, F.R., },
	booktitle={Advances in neural information processing systems },
	pages={1033--1040},
	year={2009},
	organization={NIPS Proceedings},
	url = {http://papers.nips.cc/paper/3448-supervised-dictionary-learning}
}

@inproceedings{17,
	title={Learning internal representations by error propagation},
	author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
	booktitle={},
	pages={},
	year={1985},
	organization={California Univ San Diego La Jolla Inst for Cognitive Science},
	url = {https://apps.dtic.mil/docs/citations/ADA164453}
}

@inproceedings{18,
	title={A Beginner's Guide to LSTMs and Recurrent Neural Networks},
	author={Skymind},
	booktitle={A.I. Wiki},
	pages={},
	year={2018},
	organization={Skymind},
	url = {https://skymind.ai/wiki/lstm}
}

@inproceedings{19,
	title={Recurrent Neural Networks and LSTM},
	author={Niklas Donges},
	booktitle={},
	pages={},
	year={2018},
	organization={Towards Data Science},
	url = {https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5}
}

@inproceedings{20,
	title={A Gentle Introduction to Exploding Gradients in Neural Networks},
	author={Jason Brownlee, PhD.},
	booktitle={},
	pages={},
	year={2017},
	organization={Machine Larning Mastery},
	url = {https://machinelearningmastery.com/exploding-gradients-in-neural-networks/}
}

@inproceedings{21,
	title={Recurrent Neural Networks (RNN) - The Vanishing Gradient Problem},
	author={Super Data Science Team},
	booktitle={},
	pages={},
	year={2018},
	organization={Super Data Science},
	url = {https://www.superdatascience.com/blogs/recurrent-neural-networks-rnn-the-vanishing-gradient-problem}
}

@inproceedings{22,
	title={Long short-term memory},
	author={Hochreiter, Sepp and Schmidhuber, Jurgen},
	booktitle={Neural computation, Volume 9. 8},
	pages={1735--1780},
	year={1997},
	organization={MIT Press},
	url = {https://www.bioinf.jku.at/publications/older/2604.pdf}
}

@inproceedings{23,
	title={Understanding LSTM and its diagrams},
	author={Shi Yan},
	booktitle={},
	pages={},
	year={Mar 13, 2016},
	organization={Medium},
	url = {https://medium.com/mlreview/understanding-lstm-and-its-diagrams-37e2f46f1714}
}

@inproceedings{24,
	title={Understanding LSTM Networks},
	author={Christopher Olah},
	booktitle={},
	pages={},
	year={2015},
	organization={},
	url = {https://colah.github.io/posts/2015-08-Understanding-LSTMs}
}

@inproceedings{25,
	title={Using LSTMs to forecast time-series},
	author={Ravindra Kompella},
	booktitle={},
	pages={},
	year={2018},
	organization={Towards Data Science},
	url = {https://towardsdatascience.com/using-lstms-to-forecast-time-series-4ab688386b1f}
}

@inproceedings{26,
	title={Tensorflow: A system for large-scale machine learning},
	author={Abadi, Martin and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others},
	booktitle={12th Symposium on Operating Systems Design and Implementation 16)},
	pages={265--283},
	year={2016},
	url={https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf}
}

@inproceedings{27,
	title={Optimization: Stochastic Gradient Descent},
	author={Stanford},
	booktitle={UFLDL Tutorial},
	pages={},
	year={},
	url={http://deeplearning.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent}
}

@inproceedings{28,
	title={What are differences between update rules like AdaDelta, RMSProp, AdaGrad, and Adam},
	author={Rajarshee Mitra},
	booktitle={},
	pages={},
	year={2016},
	organization={Quora},
	url={https://www.quora.com/What-are-differences-between-update-rules-like-AdaDelta-RMSProp-AdaGrad-and-AdaM}
}

@inproceedings{29,
	title={Variants of rmsprop and adagrad with logarithmic regret bounds},
	author={Mukkamala, Mahesh Chandra and Hein, Matthias},
	booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
	pages={2545--2553},
	year={2017},
	organization={JMLR.org},
	url={https://arxiv.org/pdf/1706.05507.pdf}
}

@inproceedings{30,
	title={Overview of different Optimizers for neural networks},
	author={Renu Khandelwal},
	booktitle={},
	pages={},
	year={2019},
	organization={Medium},
	url={https://medium.com/datadriveninvestor/overview-of-different-optimizers-for-neural-networks-e0ed119440c3}
}

@inproceedings{31,
	title={Adam: A method for Stochastic Optimization},
	author={Diederik P. Kingma, Jimmy Lei Ba},
	booktitle={arXiv preprint arXiv:1412.6980},
	pages={},
	year={2014},
	organization={arXiv},
	url={https://arxiv.org/pdf/1412.6980.pdf}
}

@inproceedings{32,
	title={An empirical study of the naive Bayes classifier},
	author={Rish, Irina and others},
	booktitle={IJCAI 2001 workshop on empirical methods in artificial intelligence},
	volume={3},
	number={22},
	pages={41--46},
	year={2001},
	url={https://www.cc.gatech.edu/~isbell/reading/papers/Rish.pdf}
}

@inproceedings{33,
	title={Tweepy Documentation},
	author={Joshua Roesslein},
	booktitle={},
	volume={},
	number={},
	pages={},
	year={2009},
	url={http://docs.tweepy.org/en/v3.5.0/}
}

@inproceedings{34,
	title={Tensorflow Vs. Theano: What Do Researchers Prefer As An Artificial Intelligence Framework},
	author={Srishti Deoras},
	booktitle={},
	volume={},
	number={},
	pages={},
	year={2017},
	organization={Analytics India},
	url={https://www.analyticsindiamag.com/tensorflow-vs-theano-researchers-prefer-artificial-intelligence-framework}
}