Real-Time Prediction of BITCOIN Price using Machine Learning Techniques
and Public Sentiment Analysis
- URL: http://arxiv.org/abs/2006.14473v1
- Date: Thu, 18 Jun 2020 15:40:11 GMT
- Title: Real-Time Prediction of BITCOIN Price using Machine Learning Techniques
and Public Sentiment Analysis
- Authors: S M Raju and Ali Mohammad Tarif
- Abstract summary: The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis.
Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment.
We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bitcoin is the first digital decentralized cryptocurrency that has shown a
significant increase in market capitalization in recent years. The objective of
this paper is to determine the predictable price direction of Bitcoin in USD by
machine learning techniques and sentiment analysis. Twitter and Reddit have
attracted a great deal of attention from researchers to study public sentiment.
We have applied sentiment analysis and supervised machine learning principles
to the extracted tweets from Twitter and Reddit posts, and we analyze the
correlation between bitcoin price movements and sentiments in tweets. We
explored several algorithms of machine learning using supervised learning to
develop a prediction model and provide informative analysis of future market
prices. Due to the difficulty of evaluating the exact nature of a Time
Series(ARIMA) model, it is often very difficult to produce appropriate
forecasts. Then we continue to implement Recurrent Neural Networks (RNN) with
long short-term memory cells (LSTM). Thus, we analyzed the time series model
prediction of bitcoin prices with greater efficiency using long short-term
memory (LSTM) techniques and compared the predictability of bitcoin price and
sentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE
(Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515
(multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM
with multi feature shows the more accurate result.
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