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Bitcoin Price Prediction using Tweet Sentiment and User Interaction Behaviour

by Qinan Zhu, Rotimi Ogunsakin, Laud Charles Ochei
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 43
Year of Publication: 2024
Authors: Qinan Zhu, Rotimi Ogunsakin, Laud Charles Ochei
10.5120/ijais2024451967

Qinan Zhu, Rotimi Ogunsakin, Laud Charles Ochei . Bitcoin Price Prediction using Tweet Sentiment and User Interaction Behaviour. International Journal of Applied Information Systems. 12, 43 ( Apr 2024), 23-35. DOI=10.5120/ijais2024451967

@article{ 10.5120/ijais2024451967,
author = { Qinan Zhu, Rotimi Ogunsakin, Laud Charles Ochei },
title = { Bitcoin Price Prediction using Tweet Sentiment and User Interaction Behaviour },
journal = { International Journal of Applied Information Systems },
issue_date = { Apr 2024 },
volume = { 12 },
number = { 43 },
month = { Apr },
year = { 2024 },
issn = { 2249-0868 },
pages = { 23-35 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number43/bitcoin-price-prediction-using-tweet-sentiment-and-user-interaction-behaviour/ },
doi = { 10.5120/ijais2024451967 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-29T11:40:43+05:30
%A Qinan Zhu
%A Rotimi Ogunsakin
%A Laud Charles Ochei
%T Bitcoin Price Prediction using Tweet Sentiment and User Interaction Behaviour
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 43
%P 23-35
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis has been used to predict Bitcoin prices, with results indicating relatively low prediction accuracy. User interaction behaviour such as likes, retweets, and replies have received little to no consideration as potential price prediction signals. Consequently, this paper uses both user interaction behaviour and Twitter sentiment to predict Bitcoin closing prices using multiple linear regression and Extreme Gradient Boosting (XGBoost). The predictive model outcomes are investigated using regression analysis and Shapley Additive Explanations (SHAP). Our findings indicate that using both sentiment score and user interaction behaviours significantly improves prediction accuracy, mostly during price volatility, but fails to capture Bitcoin price movement trends. Analysis of feature importance and impact on prediction outcome reveals sentiment score and user interaction behaviour play a lesser role in Bitcoin price prediction during price volatility. However, when Bitcoin prices are relatively stable, the improved accuracy is primarily due to the incorporation of user interaction behaviour. Therefore, the sentiment score is insufficient because the majority of Bitcoin-related tweets come from Bitcoin enthusiasts whose opinions are unaffected by market fluctuations. Whereas, the improved prediction accuracy observed during Bitcoin’s price volatility is attributable to increased interactions from new sets of users.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis bitcoin price prediction explainable AI Twitter sentiment

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