Impact of Twitter Sentiment Related to Bitcoin on Stock Price Returns

  • Feda Hassan Jahjah Faculty of Informatics Engineering - Al-Baath University Syria- Homs
  • Muhanad Rajab Faculty of Informatics Engineering - Al-Baath University Syria- Homs
Keywords: Sentiment Analysis, Deep Learning, Mining Social Media, Correlation, Multiple Linear Regression

Abstract

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.

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Published
2020-06-01
How to Cite
Jahjah, F. and Rajab, M. (2020) “Impact of Twitter Sentiment Related to Bitcoin on Stock Price Returns”, Journal of Engineering, 26(6), pp. 60-71. doi: 10.31026/j.eng.2020.06.05.