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We manually tagged a subset Abstract: Many traders believe in collected related data.

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These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social. Online Cryptocurrency-topic diffusion on Twitter, Telegram, and Discord This Dataset is described in Charting the Landscape of Online Cryptocurrency. Twitter sentiment has been shown to be useful in predicting whether Bitcoin's price will increase or decrease. Yet the state-of-the-art is.

account_circle Arataxe

calendar_month 09.10.2021

It is a pity, that now I can not express - I am late for a meeting. But I will return - I will necessarily write that I think on this question.
account_circle Nizil

calendar_month 13.10.2021

I am ready to help you, set questions.
account_circle Gardazshura

calendar_month 14.10.2021

Thanks for council how I can thank you?
account_circle Tausida

calendar_month 16.10.2021

You commit an error.
account_circle Faull

calendar_month 18.10.2021

Really and as I have not realized earlier

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Moreover, when it comes to the prediction of the magnitude of price change, changing the number of classes to be predicted might also result in better accuracy results. The main reason for why LSTMs seem well-suited for this specific problem is that they are inherently sequential models and thus, expected to do reasonably well at predicting a trend over time. Finally, then conclude with some directions for future work.

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Is it better to buy crypto on paypal or coinbase

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