Abstract:

Cryptocurrencies are popular today even though they do not have a physical form with their high profit rates and increasing usage day by day. However, the volatility of cryptocurrencies is higher than physical currencies. These volatilities change with the effect of social media rather than changes in exchange rates of physical currencies. For this reason, in this study, using Twitter data, one of the most widely used social media tools, real-time analysis on the values of four cryptocurrencies with the highest market value and the change in the estimated success compared to classical approaches were examined. The basic steps of this study: Obtaining Twitter data and financial data, performing sentiment analysis using Twitter data, making predictions on MM-LSTM architecture. The approach is aimed to be a predictive method open to online learning. Various filter steps were applied to remove the effect of bot users on Twitter that could prevent the prediction performance on the created data set, and the effect of the method on accuracy rate was tried to be reduced by eliminating the activity of bot accounts.

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