Real-Time Prediction of BITCOIN Price using Machine Learning Techniques\n and Public Sentiment Analysis

Abstract

Bitcoin is the first digital decentralized cryptocurrency that has shown a\nsignificant increase in market capitalization in recent years. The objective of\nthis paper is to determine the predictable price direction of Bitcoin in USD by\nmachine learning techniques and sentiment analysis. Twitter and Reddit have\nattracted a great deal of attention from researchers to study public sentiment.\nWe have applied sentiment analysis and supervised machine learning principles\nto the extracted tweets from Twitter and Reddit posts, and we analyze the\ncorrelation between bitcoin price movements and sentiments in tweets. We\nexplored several algorithms of machine learning using supervised learning to\ndevelop a prediction model and provide informative analysis of future market\nprices. Due to the difficulty of evaluating the exact nature of a Time\nSeries(ARIMA) model, it is often very difficult to produce appropriate\nforecasts. Then we continue to implement Recurrent Neural Networks (RNN) with\nlong short-term memory cells (LSTM). Thus, we analyzed the time series model\nprediction of bitcoin prices with greater efficiency using long short-term\nmemory (LSTM) techniques and compared the predictability of bitcoin price and\nsentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE\n(Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515\n(multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM\nwith multi feature shows the more accurate result.\n