Bitcoin price prediction based on sentiment analysis and LSTM

Chenfeiyu Wen; Xiangting Wu; Chuyue Shen; Zifei Huang; Peiqi Cai

doi:10.54254/2755-2721/29/20230743

Research Article

Open access

Published on 26 December 2023

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Wen,C.;Wu,X.;Shen,C.;Huang,Z.;Cai,P. (2023). Bitcoin price prediction based on sentiment analysis and LSTM. Applied and Computational Engineering,29,148-159.

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Bitcoin price prediction based on sentiment analysis and LSTM

Chenfeiyu Wen *^,1, Xiangting Wu ², Chuyue Shen ³, Zifei Huang ⁴, Peiqi Cai ⁵

¹ Central South University
² The Experimental High School Attached to Beijing Normal University
³ Hangzhou Dianzi University
⁴ The Ohio State University
⁵ Zhejiang University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/29/20230743

Abstract

As cryptocurrencies become widely accepted due to technical improvements, reliable approaches to capture their future price movements of them become critical. This study mainly combines weighted sentiment analysis results from social media-related comments and financial news headlines with a stacked LSTM model to predict second-day Bitcoin price evolution. This study also compared our results and the results produced by MLP, RF, and SVM after feeding the sentiment analysis results.

Keywords

Bitcoin, LSTM, sentiment analysis, price prediction

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References

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Cite this article

Wen,C.;Wu,X.;Shen,C.;Huang,Z.;Cai,P. (2023). Bitcoin price prediction based on sentiment analysis and LSTM. Applied and Computational Engineering,29,148-159.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.29

ISSN：2755-2721(Print) / 2755-273X(Online)

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