Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices

Abstract

This paper shows the prediction of fluctuation in the future price of cryptocurrencies. Users’ comments and tweets from twitter using Apache Flume and Price data was fetched from exchanges. Bitcoin first documented by allies Satoshi Nakamoto, the first decentralized currency payment system has gained a considerable attention in the financial system, economics, social media and computer science due to its combination of peer-to-peer nature, encryption technology, and monetary unit. Predicting the price of Bitcoin and other cryptocurrencies is a great challenge because it is immensely complex and dynamic in nature. In this paper, we have tried to predict the future price of cryptocurrencies like Bitcoin using LSTM (Long Short-Term Memory) and used Twitter data to predict public mood. By combining both market sentiment and social sentiment because bitcoin price shows mixed properties. We also have selected some other important features from the blockchain information which has a major impact on Bitcoin’s supply and demand and using them to train model that improves the predictive power of the future Bitcoin price. We have performed a deep study of how data from social media affect the price of Bitcoin and so we have included the twitter data in model training. Our model shows that how well LSTM predict the price of Bitcoin considering the high volatility. The precision given by our model is 60% and accuracy is 50%. More focus is not given to accuracy, in this case, considering the highly volatile market.