Izvorni znanstveni članak
https://doi.org/10.48188/so.6.12
Bitcoin price prediction using sentiment analysis
Dora Grubišić
orcid.org/0009-0002-8716-2661
; Faculty of Economics, University of Split, Split, Croatia; OTP banka d.d., Split, Croatia
Blanka Škrabić Perić
orcid.org/0000-0002-7448-3821
; Faculty of Economics, Department of Quantitative Methods, University of Split, Split, Croatia
Mario Jadrić
orcid.org/0000-0002-2591-3899
; Faculty of Economics, Department of Business Informatics, University of Split, Split, Croatia
*
* Dopisni autor.
Sažetak
Aim: To explore the causal relationship between social media sentiment, related behavioral factors, and bitcoin price performance, and to develop predictive models with higher accuracy in forecasting the bitcoin price by incorporating sentiment analysis.
Methods: We used the Valence Aware Dictionary and sEntiment Reasoner module to perform a sentiment analysis of 896,464 Twitter posts (tweets) published between November and December 2021, which we collected via web scraping. We created several forecasting models using the average daily sentiment polarity, the average daily number of tweets, and search interest for “bitcoin” on Google and Wikipedia as input variables. We predicted future bitcoin prices using vector autoregression (VAR), Prophet, and long short-term memory (LSTM) artificial neural network models and evaluated their predictive accuracy using the mean absolute percentage error (MAPE) as a performance measure.
Results: The results suggest a Granger causal relationship between social media sentiment and bitcoin prices. The standard VAR model achieved a MAPE of 8%, while the LSTM model had a lower error rate of 5%. The Prophet model had a MAPE of 11%.
Conclusion: Our results underline the highly speculative nature of bitcoin, especially in times of high prices. The inclusion of behavioral variables in the development of bitcoin price prediction models significantly improved their prediction accuracy, with the LSTM neural network model proving to be an extremely effective tool in this sense.
Ključne riječi
behavioral factors; bitcoin; LSTM artificial neural network model: Prophet; sentiment analysis; vector autoregression
Hrčak ID:
337622
URI
Datum izdavanja:
31.10.2025.
Posjeta: 266 *