<italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction
Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limit...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9745117/ |
| _version_ | 1818368397541376000 |
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| author | Raj Parekh Nisarg P. Patel Nihar Thakkar Rajesh Gupta Sudeep Tanwar Gulshan Sharma Innocent E. Davidson Ravi Sharma |
| author_facet | Raj Parekh Nisarg P. Patel Nihar Thakkar Rajesh Gupta Sudeep Tanwar Gulshan Sharma Innocent E. Davidson Ravi Sharma |
| author_sort | Raj Parekh |
| collection | DOAJ |
| description | Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, <italic>DL-Gues</italic>, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of <italic>Dash</italic> carried out using price history and tweets of <italic>Dash</italic>, <italic>Litecoin</italic>, and <italic>Bitcoin</italic> for various loss functions for validation. Further, to check the usability of <italic>DL-GuesS</italic> on other cryptocurrencies, we have also inferred results for price prediction of <italic>Bitcoin-Cash</italic> with the price history and tweets of <italic>Bitcoin-Cash</italic>, <italic>Litecoin</italic>, and <italic>Bitcoin</italic>. |
| first_indexed | 2024-12-13T23:07:18Z |
| format | Article |
| id | doaj.art-7d2d70a803584d21b4d6f5d396cc37c2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:07:18Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d2d70a803584d21b4d6f5d396cc37c22022-12-21T23:28:14ZengIEEEIEEE Access2169-35362022-01-0110353983540910.1109/ACCESS.2022.31633059745117<italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price PredictionRaj Parekh0https://orcid.org/0000-0001-9524-8516Nisarg P. Patel1https://orcid.org/0000-0001-5964-4204Nihar Thakkar2https://orcid.org/0000-0001-9846-5735Rajesh Gupta3https://orcid.org/0000-0003-3298-4238Sudeep Tanwar4https://orcid.org/0000-0002-1776-4651Gulshan Sharma5https://orcid.org/0000-0002-4726-0956Innocent E. Davidson6https://orcid.org/0000-0002-2336-4136Ravi Sharma7Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, IndiaDepartment of Electrical Engineering Technology, University of Johannesburg, Johannesburg, South AfricaDepartment of Electrical Power Engineering, Durban University of Technology, Steve Biko Campus, Durban, South AfricaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun, IndiaCryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, <italic>DL-Gues</italic>, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of <italic>Dash</italic> carried out using price history and tweets of <italic>Dash</italic>, <italic>Litecoin</italic>, and <italic>Bitcoin</italic> for various loss functions for validation. Further, to check the usability of <italic>DL-GuesS</italic> on other cryptocurrencies, we have also inferred results for price prediction of <italic>Bitcoin-Cash</italic> with the price history and tweets of <italic>Bitcoin-Cash</italic>, <italic>Litecoin</italic>, and <italic>Bitcoin</italic>.https://ieeexplore.ieee.org/document/9745117/Cryptocurrencycomplex systemsfusion of cryptocurrencyprice predictionVADERsentiment analysis |
| spellingShingle | Raj Parekh Nisarg P. Patel Nihar Thakkar Rajesh Gupta Sudeep Tanwar Gulshan Sharma Innocent E. Davidson Ravi Sharma <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction IEEE Access Cryptocurrency complex systems fusion of cryptocurrency price prediction VADER sentiment analysis |
| title | <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction |
| title_full | <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction |
| title_fullStr | <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction |
| title_full_unstemmed | <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction |
| title_short | <italic>DL-GuesS</italic>: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction |
| title_sort | italic dl guess italic deep learning and sentiment analysis based cryptocurrency price prediction |
| topic | Cryptocurrency complex systems fusion of cryptocurrency price prediction VADER sentiment analysis |
| url | https://ieeexplore.ieee.org/document/9745117/ |
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