DL-GuesS: Deep Learning and Sentiment Analysis-Based Cryptocurrency Price Prediction

【Author】 Parekh, Raj; Patel, Nisarg P.; Thakkar, Nihar; Gupta, Rajesh; Tanwar, Sudeep; Sharma, Gulshan; Davidson, Innocent E.; Sharma, Ravi

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】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, DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of Dash carried out using price history and tweets of Dash, Litecoin, and Bitcoin for various loss functions for validation. Further, to check the usability of DL-GuesS on other cryptocurrencies, we have also inferred results for price prediction of Bitcoin-Cash with the price history and tweets of Bitcoin-Cash, Litecoin, and Bitcoin.

【Keywords】Cryptography; Predictive models; Bitcoin; Hidden Markov models; Deep learning; Data models; Social networking (online); Cryptocurrency; complex systems; fusion of cryptocurrency; price prediction; VADER; sentiment analysis; deep learning; systems of systems

【发表时间】2022

【收录时间】2022-04-15

【文献类型】期刊

【主题类别】

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