Hybrid Deep Learning Model for Bitcoin and Ethereum Price Prediction Using Sentiment Analysis
DOI:
https://doi.org/10.26692/surjss.v57i02.7754Keywords:
Bitcoin Forcasting,, Ethereum Forcasting, , Crypto Price Prediction, , Sentiment Analysis,, Transformer, , CNN-LSTM, , Deep Learning Model;Abstract
Cryptocurrencies have upended the financial industry since they provide decentralized and peer-to-peer transactions. However, due to market volatility and the numerous non-linear relationships between price dynamics and human mood, forecasting Bitcoin values is a difficult task. The deep learning architecture shown in this work combines sentiment confidence scores derived from cryptocurrency-related tweets utilizing Transformer-based natural language processing with historical price indicators. The model incorporates Convolutional Neural Networks (CNN) to detect local time-series patterns and Long Short-Term Memory (LSTM) networks to produce long-term dependencies. We apply this architecture, involving sequence-based preprocessing and normalization, to Bitcoin and Ethereum to ensure robustness. Evaluations in comparison to baseline models Sentiment fusion dramatically increases predicting accuracy, especially during times of market turbulence, according to CNN-LSTM without sentiment, vanilla LSTM, and ARIMA. Our research helps develop scalable, sentiment-aware financial forecasting algorithms that better reflect the behavior of real markets.
References
S. Adhav, “25 CRYPTOCURRENCIES AND THE FUTURE OF FINANCE: OPPORTUNITIES AND RISKS,” Transdiscipl. THREADS, p. 223.
M. A. Fauzi, N. Paiman, and Z. Othman, “Bitcoin and cryptocurrency: Challenges, opportunities and future works,” J. Asian Financ. Econ. Bus., vol. 7, no. 8, pp. 695–704, 2020.
H. F. Atlam, A. Alenezi, M. O. Alassafi, and G. Wills, “Blockchain with internet of things: Benefits, challenges, and future directions,” Int. J. Intell. Syst. Appl., vol. 10, no. 6, pp. 40–48, 2018.
Z. Liu, X. Chen, and X. Wang, “Research on Bitcoin price prediction based on text analysis and deep learning,” in International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, 2024, pp. 58–68.
Y. Li and W. Dai, “Bitcoin price forecasting method based on CNN?LSTM hybrid neural network model,” J. Eng., vol. 2020, no. 13, pp. 344–347, 2020.
M. A. L. Khaniki and M. Manthouri, “Enhancing Price Prediction in Cryptocurrency Using Transformer Neural Network and Technical Indicators,” arXiv Prepr. arXiv2403.03606, 2024.
M. Zakhwan, M. Rafik, N. M. Shah, and A. S. B. M. Khairuddin, “Comparative Analysis of Cryptocurrency Price Prediction Using Deep Learning,” AIJR Proc., pp. 63–74, 2022.
A. R. Khurshid, “Cryptocurrency price prediction using sentiment analysis,” in Proceedings of conference. Washington, DC, USA, 2017.
A. Y. Nouira, M. Bouchakwa, and M. Amara, “Role of social networks and machine learning techniques in cryptocurrency price prediction: a survey,” Soc. Netw. Anal. Min., vol. 14, no. 1, p. 152, 2024.
B. Amirshahi and S. Lahmiri, “Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning,” Expert Syst., vol. 42, no. 1, p. e13428, 2025.
S. M. Dashtaki, M. H. Chagahi, B. Moshiri, and M. J. Piran, “Multi-Source Hard and Soft Information Fusion Approach for Accurate Cryptocurrency Price Movement Prediction,” arXiv Prepr. arXiv2409.18895, 2024.
Z. Zhang and Q. Sang, “Enhancing Bitcoin Log-return Prediction through TimesNet and Sentiment Analysis Integration,” in Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application, 2023, pp. 82–91.
R. Parekh et al., “DL-GuesS: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction,” IEEE Access, vol. 10, no. April 2021, pp. 35398–35409, 2022, doi: 10.1109/ACCESS.2022.3163305.
S. Yu, “Comparative analysis of machine learning techniques for cryptocurrency price prediction,” Appl. Comput. Eng., vol. 32, pp. 1–12, 2024.
D. Ider and S. Lessmann, “Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision,” arXiv Prepr. arXiv2204.05781, 2022.
P. Alipour and S. E. Charandabi, “The Impact of Tweet Sentiments on the Return of Cryptocurrencies: Rule-Based vs. Machine Learning Approaches,” Eur. J. Bus. Manag. Res., vol. 9, no. 1, pp. 1–5, 2024.
S. Kazeminia, H. Sajedi, and M. Arjmand, “Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM Model,” in 2023 9th International Conference on Web Research (ICWR), IEEE, 2023, pp. 173–178.
Q. Yang, Y. Sun, and Y. Wu, “Bitcoin Price Prediction Based on CNN-Bi-LSTM-Attention Model,” Highlights Business, Econ. Manag., vol. 16, pp. 80–86, 2023.
J. M. Low, Z. J. Tan, T. Y. Tang, and N. M. Salleh, “Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction,” in International Visual Informatics Conference, Springer, 2023, pp. 40–51.
K. I. Roumeliotis, N. D. Tselikas, and D. K. Nasiopoulos, “LLMs and NLP Models in Cryptocurrency Sentiment Analysis: A Comparative Classification Study,” Big Data Cogn. Comput., vol. 8, no. 6, p. 63, 2024.
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